paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
f4e1417e-c3f2-46b1-9c9f-09cd53590733 | towards-more-robust-interpretation-via-local | 2211.15900 | null | https://arxiv.org/abs/2211.15900v2 | https://arxiv.org/pdf/2211.15900v2.pdf | Towards More Robust Interpretation via Local Gradient Alignment | Neural network interpretation methods, particularly feature attribution methods, are known to be fragile with respect to adversarial input perturbations. To address this, several methods for enhancing the local smoothness of the gradient while training have been proposed for attaining \textit{robust} feature attributio... | ['Taesup Moon', 'Adrian Weller', 'Juyeon Heo', 'Seokhyeon Jeong', 'Sunghwan Joo'] | 2022-11-29 | null | null | null | null | ['network-interpretation'] | ['computer-vision'] | [ 3.68865699e-01 2.00638592e-01 -3.98801602e-02 -5.33618808e-01
-4.71644640e-01 -7.29547024e-01 5.46132863e-01 1.19982645e-01
-5.85708380e-01 6.73742414e-01 6.26369044e-02 -4.27594513e-01
-3.33138257e-01 -5.30128717e-01 -8.12521994e-01 -8.12482893e-01
4.62784171e-02 -1.79714665e-01 -6.88312873e-02 -2.07727119... | [9.022722244262695, 3.077878713607788] |
e432693a-58b0-4c08-ba54-7a5521f99e49 | text-is-all-you-need-learning-language | 2305.13731 | null | https://arxiv.org/abs/2305.13731v2 | https://arxiv.org/pdf/2305.13731v2.pdf | Text Is All You Need: Learning Language Representations for Sequential Recommendation | Sequential recommendation aims to model dynamic user behavior from historical interactions. Existing methods rely on either explicit item IDs or general textual features for sequence modeling to understand user preferences. While promising, these approaches still struggle to model cold-start items or transfer knowledge... | ['Julian McAuley', 'Jingbo Shang', 'Xin Shen', 'Jinmiao Fu', 'Jin Li', 'Ming Wang', 'Jiacheng Li'] | 2023-05-23 | null | null | null | null | ['sequential-recommendation'] | ['miscellaneous'] | [ 2.60089934e-01 -7.08791733e-01 -6.81662321e-01 -8.67893279e-01
-2.97539860e-01 -7.20941067e-01 3.27430904e-01 1.01518489e-01
-5.15770376e-01 4.92023945e-01 7.78872252e-01 -4.18226480e-01
-2.97493357e-02 -7.23884225e-01 -5.63624620e-01 -3.27321649e-01
8.66772383e-02 2.81917840e-01 -2.72519350e-01 -2.90099442... | [10.244977951049805, 5.634603023529053] |
5d6b86d5-1a3c-426c-9747-d4dddf4ee7af | cognitively-inspired-agent-based-service | 1905.12630 | null | https://arxiv.org/abs/1905.12630v1 | https://arxiv.org/pdf/1905.12630v1.pdf | Cognitively-inspired Agent-based Service Composition for Mobile & Pervasive Computing | Automatic service composition in mobile and pervasive computing faces many challenges due to the complex and highly dynamic nature of the environment. Common approaches consider service composition as a decision problem whose solution is usually addressed from optimization perspectives which are not feasible in practic... | ['Oscar J. Romero'] | 2019-05-29 | null | null | null | null | ['service-composition'] | ['miscellaneous'] | [-1.89750031e-01 2.33780757e-01 -3.49988975e-02 6.25347951e-04
-2.92931218e-02 -6.31115794e-01 8.03828001e-01 -1.05195634e-01
-1.01863071e-01 5.98218203e-01 2.09333315e-01 -4.98392200e-03
-6.83772326e-01 -7.85503268e-01 8.19251463e-02 -8.47538531e-01
-3.13110888e-01 9.69965041e-01 4.39087003e-01 -6.64340317... | [8.611757278442383, 6.919495105743408] |
62d5ed78-2a91-4fe1-851e-a102dd540029 | socialformer-social-network-inspired-long | 2202.10870 | null | https://arxiv.org/abs/2202.10870v1 | https://arxiv.org/pdf/2202.10870v1.pdf | Socialformer: Social Network Inspired Long Document Modeling for Document Ranking | Utilizing pre-trained language models has achieved great success for neural document ranking. Limited by the computational and memory requirements, long document modeling becomes a critical issue. Recent works propose to modify the full attention matrix in Transformer by designing sparse attention patterns. However, mo... | ['Zhengyi Ma', 'Huaying Yuan', 'Zhicheng Dou', 'Yujia Zhou'] | 2022-02-22 | null | null | null | null | ['document-ranking'] | ['natural-language-processing'] | [-1.68979943e-01 2.35227905e-02 -3.79732668e-01 -3.37188452e-01
-3.63682434e-02 -1.85544521e-01 6.29582047e-01 1.58623889e-01
-2.14259088e-01 3.82759422e-01 5.04618645e-01 -1.39370382e-01
-5.93170524e-01 -8.02043557e-01 -6.07301176e-01 -4.30329591e-01
-1.73866987e-01 3.96090209e-01 5.34503255e-03 -2.97224373... | [9.95290756225586, 6.587803840637207] |
f5e23873-c146-4e89-9e9d-75d4d92c3c84 | unbiased-loss-functions-for-multilabel | 2109.11282 | null | https://arxiv.org/abs/2109.11282v1 | https://arxiv.org/pdf/2109.11282v1.pdf | Unbiased Loss Functions for Multilabel Classification with Missing Labels | This paper considers binary and multilabel classification problems in a setting where labels are missing independently and with a known rate. Missing labels are a ubiquitous phenomenon in extreme multi-label classification (XMC) tasks, such as matching Wikipedia articles to a small subset out of the hundreds of thousan... | ['Rohit Babbar', 'Erik Schultheis'] | 2021-09-23 | null | null | null | null | ['extreme-multi-label-classification'] | ['methodology'] | [ 5.17175376e-01 2.31464431e-01 -4.55024660e-01 -6.16228044e-01
-1.38269353e+00 -7.00229943e-01 3.55174333e-01 4.56049979e-01
-7.18895435e-01 1.15818465e+00 -3.82345587e-01 -1.84560373e-01
-4.35330898e-01 -5.09928226e-01 -7.55806863e-01 -1.05232131e+00
1.56112507e-01 6.94537938e-01 -2.02708483e-01 2.28414491... | [9.138995170593262, 4.1970953941345215] |
9a202ac8-c55e-43b0-8a89-65eb4b25b60c | few-shot-protein-generation | 2204.01168 | null | https://arxiv.org/abs/2204.01168v1 | https://arxiv.org/pdf/2204.01168v1.pdf | Few Shot Protein Generation | We present the MSA-to-protein transformer, a generative model of protein sequences conditioned on protein families represented by multiple sequence alignments (MSAs). Unlike existing approaches to learning generative models of protein families, the MSA-to-protein transformer conditions sequence generation directly on a... | ['Tristan Bepler', 'Soumya Ram'] | 2022-04-03 | null | null | null | null | ['multiple-sequence-alignment'] | ['medical'] | [ 7.14742661e-01 1.46673694e-01 -2.35232443e-01 -6.63088381e-01
-6.90636754e-01 -9.57900643e-01 3.77993673e-01 1.38739169e-01
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-4.86714132e-02 -6.32954478e-01 -1.34883893e+00 -8.72476220e-01
-8.25696066e-02 1.17090476e+00 2.66722649e-01 -1.45997092... | [4.674692630767822, 5.608899116516113] |
39cd7def-2973-44e1-92fe-a339ceb6078a | protodiv-prototype-guided-division-of | 2304.06652 | null | https://arxiv.org/abs/2304.06652v1 | https://arxiv.org/pdf/2304.06652v1.pdf | ProtoDiv: Prototype-guided Division of Consistent Pseudo-bags for Whole-slide Image Classification | Due to the limitations of inadequate Whole-Slide Image (WSI) samples with weak labels, pseudo-bag-based multiple instance learning (MIL) appears as a vibrant prospect in WSI classification. However, the pseudo-bag dividing scheme, often crucial for classification performance, is still an open topic worth exploring. The... | ['Luping Ji', 'Pei Liu', 'Rui Yang'] | 2023-04-13 | null | null | null | null | ['multiple-instance-learning'] | ['methodology'] | [ 2.55231500e-01 7.55929872e-02 -5.21878660e-01 -6.95284188e-01
-1.16817713e+00 -2.97608286e-01 4.62224931e-01 3.19783449e-01
-4.57517594e-01 9.02576029e-01 -1.28466725e-01 -2.13594422e-01
-1.74775481e-01 -7.32658565e-01 -7.76303947e-01 -1.03312385e+00
7.48793632e-02 5.69913507e-01 4.63533401e-01 -2.27739643... | [15.106989860534668, -2.7800443172454834] |
06ca8956-26bb-4039-8005-77a556c91e27 | learning-joint-spatial-temporal | 2007.10247 | null | https://arxiv.org/abs/2007.10247v1 | https://arxiv.org/pdf/2007.10247v1.pdf | Learning Joint Spatial-Temporal Transformations for Video Inpainting | High-quality video inpainting that completes missing regions in video frames is a promising yet challenging task. State-of-the-art approaches adopt attention models to complete a frame by searching missing contents from reference frames, and further complete whole videos frame by frame. However, these approaches can su... | ['Jianlong Fu', 'Yanhong Zeng', 'Hongyang Chao'] | 2020-07-20 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2590_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123610511.pdf | eccv-2020-8 | ['seeing-beyond-the-visible', 'video-inpainting'] | ['computer-vision', 'computer-vision'] | [ 1.24032997e-01 -1.70409098e-01 -1.54413909e-01 -2.05760762e-01
-8.60710204e-01 -3.39460880e-01 3.17629278e-01 -6.39372289e-01
-2.50313014e-01 9.61194158e-01 3.71759444e-01 3.51006165e-02
1.17714994e-01 -3.24438542e-01 -1.07140803e+00 -3.91206115e-01
9.04697701e-02 -2.59656161e-01 8.48027766e-02 1.50330007... | [10.826340675354004, -1.3299099206924438] |
ff12998a-38f8-4f13-9cf1-3dfcaae828fd | cross-modal-vertical-federated-learning-for | 2306.02673 | null | https://arxiv.org/abs/2306.02673v1 | https://arxiv.org/pdf/2306.02673v1.pdf | Cross-Modal Vertical Federated Learning for MRI Reconstruction | Federated learning enables multiple hospitals to cooperatively learn a shared model without privacy disclosure. Existing methods often take a common assumption that the data from different hospitals have the same modalities. However, such a setting is difficult to fully satisfy in practical applications, since the imag... | ['Yefeng Zheng', 'Yong Xu', 'Yuexiang Li', 'Lei Zhu', 'Nanjun He', 'Yawen Huang', 'Hong Wang', 'Yunlu Yan'] | 2023-06-05 | null | null | null | null | ['mri-reconstruction', 'disentanglement'] | ['computer-vision', 'methodology'] | [ 6.99911416e-02 -2.96199452e-02 -5.20510972e-01 -7.00752616e-01
-1.31050277e+00 -5.09143054e-01 2.70081043e-01 -8.40708762e-02
-2.93865323e-01 7.58905113e-01 6.22744083e-01 1.47848465e-02
-3.65101993e-01 -3.77342016e-01 -5.54734170e-01 -1.14438796e+00
5.91998771e-02 3.10447276e-01 -4.98472780e-01 1.77216232... | [6.053690433502197, 6.430410385131836] |
bac5a5a9-e358-4c57-8d07-d54f083927b7 | stegoappdb-a-steganography-apps-forensics | 1904.09360 | null | http://arxiv.org/abs/1904.09360v1 | http://arxiv.org/pdf/1904.09360v1.pdf | StegoAppDB: a Steganography Apps Forensics Image Database | In this paper, we present a new reference dataset simulating digital evidence
for image steganography. Steganography detection is a digital image forensic
topic that is relatively unknown in practical forensics, although stego app use
in the wild is on the rise. This paper introduces the first database consisting
of mo... | [] | 2019-04-19 | null | null | null | null | ['image-steganography', 'image-forensics'] | ['computer-vision', 'computer-vision'] | [ 7.58680820e-01 5.84874349e-03 1.41709358e-01 3.73935908e-01
-7.30189025e-01 -7.73719430e-01 4.03621733e-01 -6.44180715e-01
-1.63901061e-01 5.53320348e-01 -3.08735311e-01 -7.88235664e-01
1.00406818e-01 -7.29905128e-01 -6.53872252e-01 -8.46539438e-01
-1.69350594e-01 1.68427899e-01 6.14621162e-01 -6.48725480... | [12.45722484588623, 1.0307554006576538] |
07125704-35a4-4368-942d-ce18ea9c3ea1 | lip-movements-information-disentanglement-for | 2202.06198 | null | https://arxiv.org/abs/2202.06198v2 | https://arxiv.org/pdf/2202.06198v2.pdf | Data standardization for robust lip sync | Lip sync is a fundamental audio-visual task. However, existing lip sync methods fall short of being robust to the incredible diversity of videos taken in the wild, and the majority of the diversity is caused by compound distracting factors that could degrade existing lip sync methods. To address these issues, this pape... | ['Chun Wang'] | 2022-02-13 | null | null | null | null | ['3d-face-reconstruction', 'face-model', 'face-reconstruction'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 2.08047077e-01 -1.92609012e-01 -3.67966741e-01 -2.14207754e-01
-9.30600643e-01 -5.73580503e-01 4.51831669e-01 -4.97107983e-01
-2.27433816e-01 4.45084482e-01 7.83400714e-01 2.87904084e-01
3.67823303e-01 -5.52989393e-02 -4.41008896e-01 -7.18350410e-01
2.45129392e-01 -1.00932293e-01 7.41210580e-02 1.53167367... | [13.283140182495117, -0.401765376329422] |
0406f692-023b-4acc-aaf3-2ecba2b8c291 | stock-trading-optimization-through-model-1 | 2301.09297 | null | https://arxiv.org/abs/2301.09297v3 | https://arxiv.org/pdf/2301.09297v3.pdf | Model Based Reinforcement Learning with Non-Gaussian Environment Dynamics and its Application to Portfolio Optimization | With the fast development of quantitative portfolio optimization in financial engineering, lots of AI-based algorithmic trading strategies have demonstrated promising results, among which reinforcement learning begins to manifest competitive advantages. However, the environment from real financial markets is complex an... | ['Nan Du', 'Peng Zhang', 'Jin Guo', 'Pengbo Li', 'Ting Gao', 'Huifang Huang'] | 2023-01-23 | null | null | null | null | ['algorithmic-trading', 'portfolio-optimization'] | ['time-series', 'time-series'] | [-5.36651134e-01 -5.17862201e-01 7.49278143e-02 3.06613505e-01
3.29689495e-02 -6.22055411e-01 3.00387233e-01 -1.83513220e-02
-2.34533444e-01 7.94294834e-01 2.30414383e-02 -3.90736789e-01
-5.31077445e-01 -1.09394574e+00 -5.14500320e-01 -6.63908005e-01
-7.28553057e-01 4.31332529e-01 3.34577978e-01 -4.50001657... | [4.50958776473999, 3.94915771484375] |
9c1fc679-090f-448f-80c7-3b288e849f1a | graph-based-compensated-wavelet-lifting-for-3 | 2301.04839 | null | https://arxiv.org/abs/2301.04839v1 | https://arxiv.org/pdf/2301.04839v1.pdf | Graph-based compensated wavelet lifting for 3-D+t medical CT data | An efficient scalable data representation is an important task especially in the medical area, e.g. for volumes from Computed Tomography (CT) or Magnetic Resonance Tomography (MRT), when a downscaled version of the original signal is needed. Image and video coders based on wavelet transforms provide an adequate way to ... | ['André Kaup', 'Daniela Lanz'] | 2023-01-12 | null | null | null | null | ['motion-compensation', 'data-compression'] | ['computer-vision', 'time-series'] | [ 4.76067275e-01 4.99392301e-02 1.01463340e-01 3.09503209e-02
-3.93745750e-01 7.38829151e-02 1.82518065e-01 7.10434496e-01
-6.50645077e-01 7.12482691e-01 3.71390015e-01 -2.24588588e-01
-1.99420691e-01 -1.02028000e+00 -5.39336383e-01 -7.55158246e-01
-2.88607568e-01 -1.84640456e-02 6.06011093e-01 -2.95785278... | [11.466584205627441, -2.310811758041382] |
98162fa0-f9d7-4d72-b32e-953b2055c5a6 | structural-relational-reasoning-of-point | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Duan_Structural_Relational_Reasoning_of_Point_Clouds_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Duan_Structural_Relational_Reasoning_of_Point_Clouds_CVPR_2019_paper.pdf | Structural Relational Reasoning of Point Clouds | The symmetry for the corners of a box, the continuity for the surfaces of a monitor, the linkage between the torso and other body parts --- it suggests that 3D objects may have common and underlying inner relations between local structures, and it is a fundamental ability for intelligent species to reason for them. In ... | [' Qi Tian', ' Jie Zhou', ' Jiwen Lu', ' Yu Zheng', 'Yueqi Duan'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['3d-part-segmentation'] | ['computer-vision'] | [-4.50356036e-01 3.48173201e-01 -2.80473560e-01 -6.71996057e-01
4.28228229e-01 -3.72012377e-01 5.47252536e-01 2.59499878e-01
2.33027190e-01 3.69931906e-02 -1.48138508e-01 -3.34273785e-01
-3.48981440e-01 -8.88944089e-01 -9.83602464e-01 -3.37053359e-01
-3.00597519e-01 8.39026034e-01 7.21424341e-01 -4.32347685... | [7.938223838806152, -3.3727283477783203] |
f54a47f6-03a7-4ea1-8121-1d665fcbd1d6 | multimodal-optimal-transport-based-co | 2306.08330 | null | https://arxiv.org/abs/2306.08330v1 | https://arxiv.org/pdf/2306.08330v1.pdf | Multimodal Optimal Transport-based Co-Attention Transformer with Global Structure Consistency for Survival Prediction | Survival prediction is a complicated ordinal regression task that aims to predict the ranking risk of death, which generally benefits from the integration of histology and genomic data. Despite the progress in joint learning from pathology and genomics, existing methods still suffer from challenging issues: 1) Due to t... | ['Hao Chen', 'Yingxue Xu'] | 2023-06-14 | null | null | null | null | ['whole-slide-images', 'survival-analysis'] | ['computer-vision', 'miscellaneous'] | [ 6.50018752e-02 -1.75363660e-01 -1.23995349e-01 -3.55126530e-01
-1.32165468e+00 -2.28774905e-01 3.10523570e-01 4.96329010e-01
-8.36750045e-02 4.96182233e-01 5.99805832e-01 3.25530418e-03
-5.59294164e-01 -5.49602687e-01 -6.64544225e-01 -1.22334409e+00
-2.47245386e-01 3.45604688e-01 4.70214821e-02 -9.41541269... | [15.109554290771484, -2.885674238204956] |
20e2d7da-2b10-4893-bdb6-88b93f4cc907 | an-end-to-end-progressive-multi-task-learning | null | null | https://aclanthology.org/2021.acl-long.485 | https://aclanthology.org/2021.acl-long.485.pdf | An End-to-End Progressive Multi-Task Learning Framework for Medical Named Entity Recognition and Normalization | Medical named entity recognition (NER) and normalization (NEN) are fundamental for constructing knowledge graphs and building QA systems. Existing implementations for medical NER and NEN are suffered from the error propagation between the two tasks. The mispredicted mentions from NER will directly influence the results... | ['Xiaojie Yuan', 'Ying Zhang', 'Xiangrui Cai', 'Baohang Zhou'] | 2021-08-01 | null | null | null | acl-2021-5 | ['medical-named-entity-recognition'] | ['natural-language-processing'] | [-2.58257682e-03 3.27000707e-01 -2.61115134e-01 -3.21166575e-01
-8.31113100e-01 7.48485401e-02 2.63467938e-01 2.82686144e-01
-7.63853192e-01 7.96710849e-01 4.67830271e-01 4.77315597e-02
-3.99792403e-01 -8.08589160e-01 -3.91560853e-01 -5.59415340e-01
7.03945085e-02 3.20377141e-01 3.71584415e-01 -3.33318174... | [8.65375804901123, 8.940268516540527] |
8795a8ea-36a1-4975-ab7b-3a1f1108dae6 | deep-learning-for-anomaly-detection-in-log | 2207.03820 | null | https://arxiv.org/abs/2207.03820v2 | https://arxiv.org/pdf/2207.03820v2.pdf | Deep Learning for Anomaly Detection in Log Data: A Survey | Automatic log file analysis enables early detection of relevant incidents such as system failures. In particular, self-learning anomaly detection techniques capture patterns in log data and subsequently report unexpected log event occurrences to system operators without the need to provide or manually model anomalous s... | ['Markus Wurzenberger', 'Florian Skopik', 'Sebastian Onder', 'Max Landauer'] | 2022-07-08 | null | null | null | null | ['self-learning'] | ['natural-language-processing'] | [-5.26220575e-02 -1.95855215e-01 1.30013600e-01 -3.60529393e-01
-3.25127065e-01 -3.04516405e-01 1.38073772e-01 9.95900512e-01
-1.82635993e-01 3.31092328e-01 -1.24698289e-01 -8.35646152e-01
-3.18089217e-01 -6.71959400e-01 -3.59851003e-01 -2.39720359e-01
-7.88644493e-01 5.19998431e-01 6.69146329e-02 -5.91272525... | [7.442868709564209, 2.6021761894226074] |
078cdc8a-d3ce-4f56-b461-ebb0ae7f765e | splatnet-sparse-lattice-networks-for-point | 1802.08275 | null | http://arxiv.org/abs/1802.08275v4 | http://arxiv.org/pdf/1802.08275v4.pdf | SPLATNet: Sparse Lattice Networks for Point Cloud Processing | We present a network architecture for processing point clouds that directly
operates on a collection of points represented as a sparse set of samples in a
high-dimensional lattice. Naively applying convolutions on this lattice scales
poorly, both in terms of memory and computational cost, as the size of the
lattice inc... | ['Jan Kautz', 'Ming-Hsuan Yang', 'Evangelos Kalogerakis', 'Subhransu Maji', 'Deqing Sun', 'Varun Jampani', 'Hang Su'] | 2018-02-22 | splatnet-sparse-lattice-networks-for-point-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Su_SPLATNet_Sparse_Lattice_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Su_SPLATNet_Sparse_Lattice_CVPR_2018_paper.pdf | cvpr-2018-6 | ['3d-part-segmentation'] | ['computer-vision'] | [-7.61562288e-02 1.78635553e-01 1.64814726e-01 -4.94744211e-01
-5.52487314e-01 -6.08150840e-01 5.75458288e-01 1.29202664e-01
-4.27061796e-01 3.77148241e-02 6.11003069e-03 -1.95574716e-01
6.44964278e-02 -1.31482399e+00 -1.19548571e+00 -1.73481569e-01
-4.13004458e-01 9.03814554e-01 6.30308330e-01 1.90856103... | [7.9777607917785645, -3.580671548843384] |
a87372ff-22b9-4fa4-a65f-38fee36c3012 | blank-collapse-compressing-ctc-emission-for | 2210.17017 | null | https://arxiv.org/abs/2210.17017v2 | https://arxiv.org/pdf/2210.17017v2.pdf | Blank Collapse: Compressing CTC emission for the faster decoding | Connectionist Temporal Classification (CTC) model is a very efficient method for modeling sequences, especially for speech data. In order to use CTC model as an Automatic Speech Recognition (ASR) task, the beam search decoding with an external language model like n-gram LM is necessary to obtain reasonable results. In ... | ['Soonshin Seo', 'Seunghyun Seo', 'Ohhyeok Kwon', 'Minkyu Jung'] | 2022-10-31 | null | null | null | null | ['mathematical-reasoning'] | ['natural-language-processing'] | [ 2.17377797e-01 -2.46855944e-01 -2.44152904e-01 -2.84635156e-01
-7.79910922e-01 -3.95574331e-01 6.23916924e-01 -1.52578741e-01
-7.47504056e-01 6.71166420e-01 1.52208939e-01 -9.70829844e-01
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1.33076906e-01 5.62202990e-01 4.18136865e-01 -3.84842128... | [14.410144805908203, 6.866108417510986] |
fd4e7c47-c3b0-46e1-a554-168e181f82ab | alma-alternating-minimization-algorithm-for | 2102.10226 | null | https://arxiv.org/abs/2102.10226v4 | https://arxiv.org/pdf/2102.10226v4.pdf | ALMA: Alternating Minimization Algorithm for Clustering Mixture Multilayer Network | The paper considers a Mixture Multilayer Stochastic Block Model (MMLSBM), where layers can be partitioned into groups of similar networks, and networks in each group are equipped with a distinct Stochastic Block Model. The goal is to partition the multilayer network into clusters of similar layers, and to identify comm... | ['Teng Zhang', 'Feng Yu', 'Marianna Pensky', 'Xing Fan'] | 2021-02-20 | null | null | null | null | ['stochastic-block-model'] | ['graphs'] | [-2.74539441e-01 -4.19921242e-02 -1.89997822e-01 1.09089337e-01
-8.58381242e-02 -4.25693184e-01 4.72194493e-01 -3.16766948e-01
1.04348913e-01 4.53126967e-01 1.55553102e-01 -1.96522951e-01
-4.85291451e-01 -6.95187747e-01 -3.01347941e-01 -9.98369992e-01
-2.54271954e-01 6.24960959e-01 2.78730452e-01 2.16496900... | [6.982827186584473, 5.223666191101074] |
cb5e238d-99b4-41eb-abca-898179545d23 | ddcolor-towards-photo-realistic-and-semantic | 2212.11613 | null | https://arxiv.org/abs/2212.11613v3 | https://arxiv.org/pdf/2212.11613v3.pdf | DDColor: Towards Photo-Realistic and Semantic-Aware Image Colorization via Dual Decoders | Automatic image colorization is a challenging problem. Due to the high illness and multi-modal uncertainty, directly training a deep neural network usually leads to incorrect semantic colors and low color richness. Recent transformer-based methods can deliver better results, but they often rely on manually designed pri... | ['Xuansong Xie', 'Lingzhi Li', 'Peiran Ren', 'Wenqi Ouyang', 'Tao Yang', 'Xiaoyang Kang'] | 2022-12-22 | null | null | null | null | ['colorization'] | ['computer-vision'] | [ 2.94854920e-02 -2.41408437e-01 4.28234451e-02 -2.64396846e-01
-8.88354897e-01 -6.00842655e-01 3.96708786e-01 -1.06812492e-02
-3.27777267e-01 4.68937516e-01 9.64663476e-02 -9.59462672e-02
2.66797334e-01 -7.00291693e-01 -7.15046108e-01 -7.61748731e-01
5.47408819e-01 6.26914203e-03 2.83912003e-01 -1.14592239... | [11.422965049743652, -1.0267001390457153] |
3511d42c-4b17-4fb6-a14f-44531248d119 | feature-aligned-n-beats-with-sinkhorn | 2305.15196 | null | https://arxiv.org/abs/2305.15196v1 | https://arxiv.org/pdf/2305.15196v1.pdf | Feature-aligned N-BEATS with Sinkhorn divergence | In this study, we propose Feature-aligned N-BEATS as a domain generalization model for univariate time series forecasting problems. The proposed model is an extension of the doubly residual stacking architecture of N-BEATS (Oreshkin et al. [34]) into a representation learning framework. The model is a new structure tha... | ['Kyunghyun Park', 'Joonhun Lee', 'Myungjoo Kang', 'Myeongho Jeon'] | 2023-05-24 | null | null | null | null | ['univariate-time-series-forecasting'] | ['time-series'] | [ 3.67809981e-01 -1.54701009e-01 -4.00807150e-02 -7.32780993e-01
-5.72273076e-01 -7.06553400e-01 6.44709289e-01 -8.31434131e-02
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-5.10813475e-01 -4.96568501e-01 -7.72182524e-01 -8.93287063e-01
-5.71985960e-01 1.80383131e-01 -1.55649306e-02 -4.24014151... | [7.100034236907959, 3.226264715194702] |
4d782e5c-268e-4721-8130-9d0c7f0c5aa5 | human-activity-recognition-from-wi-fi-csi | 2212.13161 | null | https://arxiv.org/abs/2212.13161v1 | https://arxiv.org/pdf/2212.13161v1.pdf | Human Activity Recognition from Wi-Fi CSI Data Using Principal Component-Based Wavelet CNN | Human Activity Recognition (HAR) is an emerging technology with several applications in surveillance, security, and healthcare sectors. Noninvasive HAR systems based on Wi-Fi Channel State Information (CSI) signals can be developed leveraging the quick growth of ubiquitous Wi-Fi technologies, and the correlation betwee... | ['Hafiz Imtiaz', 'Tahsina Farah Sanam', 'Ishtiaque Ahmed Showmik'] | 2022-12-26 | null | null | null | null | ['human-activity-recognition', 'human-activity-recognition'] | ['computer-vision', 'time-series'] | [ 4.03706431e-01 -6.98485225e-02 -2.03147888e-01 1.05519608e-01
-3.37721556e-01 -8.14610869e-02 1.89783081e-01 -4.07362401e-01
-5.16786933e-01 7.46761501e-01 4.74081844e-01 -3.17692190e-01
-2.73981661e-01 -7.72481382e-01 -4.21456158e-01 -8.18376541e-01
-7.36276925e-01 -2.04914048e-01 3.46233368e-01 -7.77581409... | [7.1412177085876465, 0.7266372442245483] |
dbc94ac0-6cdf-439c-a3bf-d81a5d06579a | 190600318 | 1906.00318 | null | https://arxiv.org/abs/1906.00318v1 | https://arxiv.org/pdf/1906.00318v1.pdf | Question Answering as an Automatic Evaluation Metric for News Article Summarization | Recent work in the field of automatic summarization and headline generation focuses on maximizing ROUGE scores for various news datasets. We present an alternative, extrinsic, evaluation metric for this task, Answering Performance for Evaluation of Summaries. APES utilizes recent progress in the field of reading-compre... | ['Tal Baumel', 'Matan Eyal', 'Michael Elhadad'] | 2019-06-02 | question-answering-as-an-automatic-evaluation | https://aclanthology.org/N19-1395 | https://aclanthology.org/N19-1395.pdf | naacl-2019-6 | ['headline-generation'] | ['natural-language-processing'] | [ 3.41584086e-01 7.09689021e-01 -1.58789620e-01 -3.77010018e-01
-1.64208245e+00 -5.91488779e-01 8.90929222e-01 6.65242374e-01
-5.48491359e-01 1.22059464e+00 1.30814385e+00 -2.70380914e-01
-1.83881283e-01 -5.13753057e-01 -6.43507123e-01 8.45475644e-02
1.32673681e-01 5.75728655e-01 2.31494054e-01 -3.22896391... | [12.45904541015625, 9.448386192321777] |
46072dcb-1800-4848-8f3d-20cbcdae1a83 | benchmarking-machine-learning-models-on-eicu | 1910.00964 | null | https://arxiv.org/abs/1910.00964v3 | https://arxiv.org/pdf/1910.00964v3.pdf | Benchmarking machine learning models on multi-centre eICU critical care dataset | Progress of machine learning in critical care has been difficult to track, in part due to absence of public benchmarks. Other fields of research (such as computer vision and natural language processing) have established various competitions and public benchmarks. Recent availability of large clinical datasets has enabl... | ['Vevake Balaraman', 'Seyedmostafa Sheikhalishahi', 'Venet Osmani'] | 2019-10-02 | null | null | null | null | ['patient-phenotyping'] | ['medical'] | [ 1.64828122e-01 -4.49847095e-02 -1.23984836e-01 -3.60059112e-01
-1.06502712e+00 -4.54258263e-01 2.33274013e-01 1.03227794e+00
-7.95360386e-01 1.00096858e+00 4.40053791e-01 -5.38405955e-01
-4.62474763e-01 -5.62623441e-01 -2.52908349e-01 -6.71928644e-01
-4.50352877e-01 1.01261103e+00 -2.19636440e-01 2.23641858... | [7.969470977783203, 6.272236347198486] |
7c7b0f04-5573-45a3-84b1-5fe5cf0d22de | x-2-vlm-all-in-one-pre-trained-model-for | 2211.12402 | null | https://arxiv.org/abs/2211.12402v1 | https://arxiv.org/pdf/2211.12402v1.pdf | X$^2$-VLM: All-In-One Pre-trained Model For Vision-Language Tasks | Vision language pre-training aims to learn alignments between vision and language from a large amount of data. We proposed multi-grained vision language pre-training, a unified approach which can learn vision language alignments in multiple granularity. This paper advances the proposed method by unifying image and vide... | ['Wangchunshu Zhou', 'Jipeng Zhang', 'Jiawei Wang', 'Hang Li', 'Xinsong Zhang', 'Yan Zeng'] | 2022-11-22 | null | null | null | null | ['video-question-answering', 'visual-reasoning', 'xlm-r', 'visual-reasoning'] | ['computer-vision', 'computer-vision', 'natural-language-processing', 'reasoning'] | [-7.69673884e-02 -4.62280735e-02 -2.01683551e-01 -3.87827426e-01
-1.18989015e+00 -3.47584695e-01 7.71276772e-01 -2.95646757e-01
-6.69067979e-01 4.99539822e-01 1.00819267e-01 -3.74699742e-01
6.27862692e-01 -5.64047694e-01 -1.25931931e+00 -4.09767807e-01
3.00283194e-01 5.60330033e-01 2.16162875e-01 -1.37850955... | [11.044230461120605, 1.546820044517517] |
546e07c2-7f51-4fed-9d10-eca0b15eb291 | parameter-efficient-fine-tuning-of-llama-for | 2307.03042 | null | https://arxiv.org/abs/2307.03042v1 | https://arxiv.org/pdf/2307.03042v1.pdf | Parameter-Efficient Fine-Tuning of LLaMA for the Clinical Domain | Adapting pretrained language models to novel domains, such as clinical applications, traditionally involves retraining their entire set of parameters. However, this approach is increasingly proven to be impractical owing to the substantial computational requirements associated with training such large language models. ... | ['Beatrice Alex', 'Pasquale Minervini', 'Luke Daines', 'Aryo Gema'] | 2023-07-06 | null | null | null | null | ['domain-adaptation'] | ['methodology'] | [ 2.77757555e-01 2.31918871e-01 -3.96834552e-01 -5.46928644e-01
-1.42956734e+00 -6.00090206e-01 3.25861275e-01 6.55297458e-01
-7.15454817e-01 9.42240059e-01 2.83147097e-01 -5.49433768e-01
-3.13895136e-01 -4.93327081e-01 -4.68360990e-01 -7.02728510e-01
-1.38204172e-01 7.75747418e-01 -6.11793324e-02 1.58415660... | [8.537708282470703, 8.657710075378418] |
0c59d953-f96b-4406-9dfc-c0a42b57f367 | visual-saliency-transformer | 2104.12099 | null | https://arxiv.org/abs/2104.12099v2 | https://arxiv.org/pdf/2104.12099v2.pdf | Visual Saliency Transformer | Existing state-of-the-art saliency detection methods heavily rely on CNN-based architectures. Alternatively, we rethink this task from a convolution-free sequence-to-sequence perspective and predict saliency by modeling long-range dependencies, which can not be achieved by convolution. Specifically, we develop a novel ... | ['Junwei Han', 'Ling Shao', 'Kaiyuan Wan', 'Ni Zhang', 'Nian Liu'] | 2021-04-25 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Liu_Visual_Saliency_Transformer_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Liu_Visual_Saliency_Transformer_ICCV_2021_paper.pdf | iccv-2021-1 | ['rgb-d-salient-object-detection', 'thermal-image-segmentation'] | ['computer-vision', 'computer-vision'] | [ 4.03772950e-01 -1.04317293e-01 -1.80059031e-01 -4.20855284e-01
-9.45707858e-01 -2.51428992e-01 5.42758226e-01 -1.19840994e-01
-1.76049799e-01 4.36156511e-01 2.51624048e-01 -1.71969503e-01
4.90725875e-01 -6.43528044e-01 -1.07604814e+00 -6.33730650e-01
5.13124108e-01 -2.39305750e-01 9.47997451e-01 -3.66678864... | [9.696333885192871, -0.3881734609603882] |
755e886a-54dd-4946-ac7b-ad207bbc22ba | masked-metaphor-modeling-to-transfer-literal | 2210.04756 | null | https://arxiv.org/abs/2210.04756v2 | https://arxiv.org/pdf/2210.04756v2.pdf | Metaphorical Paraphrase Generation: Feeding Metaphorical Language Models with Literal Texts | This study presents a new approach to metaphorical paraphrase generation by masking literal tokens of literal sentences and unmasking them with metaphorical language models. Unlike similar studies, the proposed algorithm does not only focus on verbs but also on nouns and adjectives. Despite the fact that the transfer r... | ['John Pavlopoulos', 'Giorgio Ottolina'] | 2022-10-10 | null | null | null | null | ['paraphrase-generation', 'sentence-classification', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing', 'natural-language-processing'] | [ 2.60812521e-01 4.92444903e-01 -1.42622992e-01 -9.06962231e-02
-1.10819973e-01 -7.81769454e-01 1.04770768e+00 3.18219155e-01
-6.29722774e-01 8.59327912e-01 4.95493531e-01 -4.94314164e-01
2.80302823e-01 -9.71435189e-01 -4.00649697e-01 -5.31060278e-01
3.77643347e-01 6.47008061e-01 -3.01884055e-01 -9.30884063... | [11.02902603149414, 9.157095909118652] |
5698e2be-349e-4e94-a21b-957cd8d3f2aa | towards-expressive-speaking-style-modelling | 2203.12201 | null | https://arxiv.org/abs/2203.12201v2 | https://arxiv.org/pdf/2203.12201v2.pdf | Towards Expressive Speaking Style Modelling with Hierarchical Context Information for Mandarin Speech Synthesis | Previous works on expressive speech synthesis mainly focus on current sentence. The context in adjacent sentences is neglected, resulting in inflexible speaking style for the same text, which lacks speech variations. In this paper, we propose a hierarchical framework to model speaking style from context. A hierarchical... | ['Helen Meng', 'Shiyin Kang', 'Zhiyong Wu', 'Liyang Chen', 'Yixuan Zhou', 'Shun Lei'] | 2022-03-23 | null | null | null | null | ['expressive-speech-synthesis'] | ['speech'] | [ 2.55509198e-01 7.19532743e-02 -2.82088578e-01 -7.15078473e-01
-4.30410087e-01 -4.71625715e-01 4.32114780e-01 -3.36185336e-01
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6.12690270e-01 -3.62656891e-01 -6.71967193e-02 -3.90423149... | [14.865497589111328, 6.598909378051758] |
fd59083e-5891-4297-967c-e892903e48f0 | dbgdgm-dynamic-brain-graph-deep-generative | 2301.11408 | null | https://arxiv.org/abs/2301.11408v1 | https://arxiv.org/pdf/2301.11408v1.pdf | DBGDGM: Dynamic Brain Graph Deep Generative Model | Graphs are a natural representation of brain activity derived from functional magnetic imaging (fMRI) data. It is well known that clusters of anatomical brain regions, known as functional connectivity networks (FCNs), encode temporal relationships which can serve as useful biomarkers for understanding brain function an... | ['Pietro Lio', 'Nicola Toschi', 'Simeon Spasov', 'Alexander Campbell'] | 2023-01-26 | null | null | null | null | ['dynamic-link-prediction', 'graph-classification'] | ['graphs', 'graphs'] | [-1.64789334e-02 3.37194115e-01 1.06482003e-02 -3.01481485e-01
4.88045573e-01 -6.54308736e-01 7.99403965e-01 2.78444231e-01
-2.97645718e-01 3.61835301e-01 5.77843010e-01 -1.54010788e-01
-4.06011730e-01 -8.54359150e-01 -3.82218421e-01 -6.45295858e-01
-8.76898885e-01 8.51039052e-01 1.89046860e-01 1.36090010... | [12.363861083984375, 3.414086103439331] |
df504d32-b687-4cfd-a91c-60338feec913 | single-stage-is-enough-multi-person-absolute | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Jin_Single-Stage_Is_Enough_Multi-Person_Absolute_3D_Pose_Estimation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Jin_Single-Stage_Is_Enough_Multi-Person_Absolute_3D_Pose_Estimation_CVPR_2022_paper.pdf | Single-Stage Is Enough: Multi-Person Absolute 3D Pose Estimation | The existing multi-person absolute 3D pose estimation methods are mainly based on two-stage paradigm, i.e., top-down or bottom-up, leading to redundant pipelines with high computation cost. We argue that it is more desirable to simplify such two-stage paradigm to a single-stage one to promote both efficiency and pe... | ['Jian Zhao', 'Xuecheng Nie', 'Yandong Guo', 'Yabo Xiao', 'Xiaojuan Wang', 'Chenyang Xu', 'Lei Jin'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['3d-pose-estimation'] | ['computer-vision'] | [-2.21707955e-01 3.82736996e-02 -3.25489372e-01 -4.77216244e-01
-8.73629391e-01 -3.29778612e-01 3.15150976e-01 -9.12697092e-02
-3.42635751e-01 2.83442646e-01 3.99510592e-01 3.27415794e-01
1.62858620e-01 -7.85374403e-01 -4.51452672e-01 -3.92725796e-01
1.23252548e-01 6.10046983e-01 2.91237712e-01 -3.49468976... | [6.990878582000732, -0.9788516163825989] |
243267cd-710b-493a-9b61-642c72f4b888 | introducing-fuzzy-layers-for-deep-learning | 2003.00880 | null | https://arxiv.org/abs/2003.00880v1 | https://arxiv.org/pdf/2003.00880v1.pdf | Introducing Fuzzy Layers for Deep Learning | Many state-of-the-art technologies developed in recent years have been influenced by machine learning to some extent. Most popular at the time of this writing are artificial intelligence methodologies that fall under the umbrella of deep learning. Deep learning has been shown across many applications to be extremely po... | ['Derek T. Anderson', 'Stanton R. Price', 'Steven R. Price'] | 2020-02-21 | null | null | null | null | ['road-segementation'] | ['computer-vision'] | [ 1.21604301e-01 -1.99697353e-03 5.97223267e-02 -5.79214692e-01
5.58897294e-02 -2.51240313e-01 5.32243967e-01 -9.20736864e-02
-5.84498644e-01 7.55134165e-01 -5.45027196e-01 -3.69761765e-01
-3.61234516e-01 -1.21429086e+00 -6.30284965e-01 -6.05400383e-01
-2.47794483e-02 3.86870891e-01 3.67949575e-01 -6.67393446... | [9.146449089050293, 2.0251474380493164] |
60071211-8823-4810-b1fc-e5d053c924b6 | efficient-differentiable-quadratic | 2112.07464 | null | https://arxiv.org/abs/2112.07464v1 | https://arxiv.org/pdf/2112.07464v1.pdf | Efficient differentiable quadratic programming layers: an ADMM approach | Recent advances in neural-network architecture allow for seamless integration of convex optimization problems as differentiable layers in an end-to-end trainable neural network. Integrating medium and large scale quadratic programs into a deep neural network architecture, however, is challenging as solving quadratic pr... | ['Roy Kwon', 'Andrew Butler'] | 2021-12-14 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [-2.20762581e-01 7.09001273e-02 1.34126410e-01 -4.14173454e-01
-7.37313449e-01 -5.47290504e-01 1.63181469e-01 -9.88963246e-02
-7.99247861e-01 8.81180167e-01 -2.70373613e-01 -6.99995220e-01
-3.14708978e-01 -5.04150510e-01 -9.62929845e-01 -5.97301543e-01
-1.69613734e-01 5.94729066e-01 -4.17688280e-01 -3.15120041... | [6.946139335632324, 3.68186092376709] |
09cfad47-38d8-449d-bfc8-e5081f299550 | flexible-domain-adaptation-for-automated | null | null | https://aclanthology.org/D15-1049 | https://aclanthology.org/D15-1049.pdf | Flexible Domain Adaptation for Automated Essay Scoring Using Correlated Linear Regression | null | ['Ph', 'Peter i', 'Kian Ming A. Chai', 'Hwee Tou Ng'] | 2015-09-01 | null | null | null | emnlp-2015-9 | ['automated-essay-scoring'] | ['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... | [-7.411994457244873, 3.781294345855713] |
1bda8cf7-96f8-4aad-9362-f278f743b468 | optimal-task-and-motion-planning-and | 2303.14874 | null | https://arxiv.org/abs/2303.14874v1 | https://arxiv.org/pdf/2303.14874v1.pdf | Optimal task and motion planning and execution for human-robot multi-agent systems in dynamic environments | Combining symbolic and geometric reasoning in multi-agent systems is a challenging task that involves planning, scheduling, and synchronization problems. Existing works overlooked the variability of task duration and geometric feasibility that is intrinsic to these systems because of the interaction between agents and ... | ['Nicola Pedrocchi', 'Amedeo Cesta', 'Andrea Orlandini', 'Manuel Beschi', 'Alessandro Umbrico', 'Marco Faroni'] | 2023-03-27 | null | null | null | null | ['motion-planning'] | ['robots'] | [ 3.81425619e-01 3.02081764e-01 7.65022039e-02 1.42079845e-01
1.22849658e-01 -8.87376308e-01 5.47958314e-01 2.32678086e-01
-4.59623709e-02 7.36410022e-01 -2.41507858e-01 -1.47272155e-01
-8.95864010e-01 -6.97611749e-01 -4.45950031e-01 -4.56959158e-01
-2.87904263e-01 8.59886467e-01 4.23907578e-01 -3.13125402... | [4.933913707733154, 1.6082154512405396] |
23241c39-e6ad-4d33-a515-b318eb7698bd | labelbench-a-comprehensive-framework-for | 2306.09910 | null | https://arxiv.org/abs/2306.09910v1 | https://arxiv.org/pdf/2306.09910v1.pdf | LabelBench: A Comprehensive Framework for Benchmarking Label-Efficient Learning | Labeled data are critical to modern machine learning applications, but obtaining labels can be expensive. To mitigate this cost, machine learning methods, such as transfer learning, semi-supervised learning and active learning, aim to be label-efficient: achieving high predictive performance from relatively few labeled... | ['Robert D Nowak', 'Kevin Jamieson', 'Simon Shaolei Du', 'Yinglun Zhu', 'Stephen Mussmann', 'Gregory Canal', 'Yifang Chen', 'Jifan Zhang'] | 2023-06-16 | null | null | null | null | ['active-learning', 'benchmarking', 'active-learning', 'benchmarking'] | ['methodology', 'miscellaneous', 'natural-language-processing', 'robots'] | [ 2.55574137e-01 6.76018000e-02 -7.55348325e-01 -6.08900964e-01
-1.36249101e+00 -7.56536305e-01 4.99182165e-01 3.83986026e-01
-6.29421413e-01 7.48589098e-01 -1.61192641e-01 -1.06571816e-01
-1.76284388e-02 -4.49865580e-01 -5.80340385e-01 -6.02860451e-01
1.18383735e-01 9.77305353e-01 3.40033263e-01 4.44680333... | [9.571476936340332, 4.066695690155029] |
44f06a81-8659-4849-99b2-9b982a77d3e4 | the-unknown-knowns-a-graph-based-approach-for | null | null | https://www.emerald.com/insight/content/doi/10.1108/OIR-12-2020-0562/full/html | https://www.researchgate.net/publication/350180428_The_unknown_knowns_a_graph-based_approach_for_temporal_COVID-19_literature_mining | The unknown knowns: a graph-based approach for temporal COVID-19 literature mining | Purpose
The COVID-19 pandemic has sparked a remarkable volume of research literature, and scientists are increasingly in need of intelligent tools to cut through the noise and uncover relevant research directions. As a response, the authors propose a novel framework. In this framework, the authors develop a novel weig... | ['Lamia Benhiba', 'Aqil Assalil', 'Runia Roy', 'Ulya Bayram'] | 2021-03-23 | null | null | null | online-information-review-2021-3 | ['literature-mining'] | ['natural-language-processing'] | [-9.22344849e-02 7.46081844e-02 -8.10246825e-01 2.02705562e-01
1.53440624e-01 -6.93352461e-01 6.53674066e-01 5.73066831e-01
-2.31267884e-01 5.33948421e-01 5.60335755e-01 -7.12874591e-01
-8.20930421e-01 -1.05805314e+00 -6.10958397e-01 -3.36429849e-02
-8.36933851e-02 3.65812093e-01 -1.17419295e-01 -1.76796213... | [9.499428749084473, 8.201813697814941] |
f96b4933-3187-468a-b6b9-f5e6152b9fe1 | a-thousand-words-are-worth-more-than-a | 2201.05299 | null | https://arxiv.org/abs/2201.05299v1 | https://arxiv.org/pdf/2201.05299v1.pdf | A Thousand Words Are Worth More Than a Picture: Natural Language-Centric Outside-Knowledge Visual Question Answering | Outside-knowledge visual question answering (OK-VQA) requires the agent to comprehend the image, make use of relevant knowledge from the entire web, and digest all the information to answer the question. Most previous works address the problem by first fusing the image and question in the multi-modal space, which is in... | ['Prem Natarajan', 'Ying Nian Wu', 'Aishwarya Reganti', 'Govind Thattai', 'Qing Ping', 'Feng Gao'] | 2022-01-14 | null | null | null | null | ['generative-question-answering'] | ['natural-language-processing'] | [ 1.74127176e-01 3.82155955e-01 -1.93241891e-02 -1.10126004e-01
-1.27321827e+00 -9.42567587e-01 8.83951664e-01 -1.30429879e-01
-3.07528347e-01 6.72475338e-01 2.49384716e-01 -4.71122712e-01
2.01495141e-01 -9.69659090e-01 -8.86429429e-01 -5.27269542e-01
7.39820898e-01 5.65466881e-01 4.19228137e-01 -3.13807517... | [10.904330253601074, 1.5547198057174683] |
54f98915-8872-4731-88ae-bd4af4414a0b | sar-generalization-of-physiological-agility | 2307.03716 | null | https://arxiv.org/abs/2307.03716v1 | https://arxiv.org/pdf/2307.03716v1.pdf | SAR: Generalization of Physiological Agility and Dexterity via Synergistic Action Representation | Learning effective continuous control policies in high-dimensional systems, including musculoskeletal agents, remains a significant challenge. Over the course of biological evolution, organisms have developed robust mechanisms for overcoming this complexity to learn highly sophisticated strategies for motor control. Wh... | ['Vikash Kumar', 'Vittorio Caggiano', 'Cameron Berg'] | 2023-07-07 | null | null | null | null | ['continuous-control'] | ['playing-games'] | [ 2.49083340e-01 1.90852061e-01 -2.16552347e-01 2.06784025e-01
-4.10864234e-01 -6.11757636e-01 5.76419473e-01 -4.03423905e-01
-6.28565669e-01 1.14781857e+00 5.39108086e-03 1.02393642e-01
-4.84089077e-01 -3.46822947e-01 -9.80365813e-01 -6.99229598e-01
-5.97389579e-01 5.14349043e-01 2.49747753e-01 -6.56563282... | [4.459732532501221, 1.1922955513000488] |
525c0f7a-f2de-46cd-ae0c-947b04d2bdba | investigating-lexical-sharing-in-multilingual | 2305.03207 | null | https://arxiv.org/abs/2305.03207v1 | https://arxiv.org/pdf/2305.03207v1.pdf | Investigating Lexical Sharing in Multilingual Machine Translation for Indian Languages | Multilingual language models have shown impressive cross-lingual transfer ability across a diverse set of languages and tasks. To improve the cross-lingual ability of these models, some strategies include transliteration and finer-grained segmentation into characters as opposed to subwords. In this work, we investigate... | ['Rachel Bawden', 'Sonal Sannigrahi'] | 2023-05-04 | null | null | null | null | ['transliteration', 'cross-lingual-transfer'] | ['natural-language-processing', 'natural-language-processing'] | [-1.82297137e-02 -3.29591453e-01 -3.51551175e-01 -5.52740812e-01
-1.12201524e+00 -1.25580704e+00 8.61782551e-01 -8.69533792e-02
-7.11059213e-01 1.10341978e+00 3.64228845e-01 -9.93017077e-01
2.64644742e-01 -3.78092110e-01 -7.67046869e-01 -3.71607333e-01
4.40394729e-01 8.25291038e-01 -4.13278863e-03 -4.08172190... | [11.314047813415527, 10.280728340148926] |
3644a67b-4708-44b8-bf99-32fbf3757ba5 | adaptive-multi-scale-online-likelihood | 2303.13696 | null | https://arxiv.org/abs/2303.13696v1 | https://arxiv.org/pdf/2303.13696v1.pdf | Adaptive Multi-scale Online Likelihood Network for AI-assisted Interactive Segmentation | Existing interactive segmentation methods leverage automatic segmentation and user interactions for label refinement, significantly reducing the annotation workload compared to manual annotation. However, these methods lack quick adaptability to ambiguous and noisy data, which is a challenge in CT volumes containing lu... | ['Tom Vercauteren', "Jan D'hooge", 'Jan Deprest', 'Sarim Ather', 'Indrajeet Mandal', 'Helena Williams', 'Muhammad Asad'] | 2023-03-23 | null | null | null | null | ['interactive-segmentation'] | ['computer-vision'] | [ 1.55354232e-01 1.25681087e-01 -3.12875479e-01 -5.89851439e-01
-1.40594625e+00 -5.78287244e-01 -3.56749296e-01 5.65914571e-01
-7.62609124e-01 6.63898230e-01 9.03039724e-02 -4.30533618e-01
-2.85054535e-01 -2.10755542e-01 -4.40951943e-01 -6.50919378e-01
8.95939097e-02 8.64248335e-01 7.78864861e-01 5.04264235... | [14.677878379821777, -2.264492988586426] |
e2932e6f-c4f8-4b79-befd-59b60a750a63 | direct-speech-to-speech-translation-without | 2212.05805 | null | https://arxiv.org/abs/2212.05805v1 | https://arxiv.org/pdf/2212.05805v1.pdf | Direct Speech-to-speech Translation without Textual Annotation using Bottleneck Features | Speech-to-speech translation directly translates a speech utterance to another between different languages, and has great potential in tasks such as simultaneous interpretation. State-of-art models usually contains an auxiliary module for phoneme sequences prediction, and this requires textual annotation of the trainin... | ['Zejun Ma', 'Xiang Yin', 'Junjie Pan', 'Junhui Zhang'] | 2022-12-12 | null | null | null | null | ['speech-to-speech-translation'] | ['speech'] | [ 5.76242566e-01 2.25054204e-01 -3.60806912e-01 -5.88090241e-01
-1.01809132e+00 -5.63537955e-01 7.93992281e-01 -3.20781589e-01
-2.19912529e-01 6.62970185e-01 1.63122118e-01 -9.51650918e-01
6.73804045e-01 -3.65893304e-01 -7.56417990e-01 -5.16695201e-01
6.41188681e-01 6.24974489e-01 1.85987204e-01 -3.06604832... | [14.504342079162598, 7.143311023712158] |
68cdf3f6-8504-40fe-9533-ee1daf03545b | dual-path-attention-is-all-you-need-for-audio | 2207.04213 | null | https://arxiv.org/abs/2207.04213v2 | https://arxiv.org/pdf/2207.04213v2.pdf | Dual-Path Cross-Modal Attention for better Audio-Visual Speech Extraction | Audio-visual target speech extraction, which aims to extract a certain speaker's speech from the noisy mixture by looking at lip movements, has made significant progress combining time-domain speech separation models and visual feature extractors (CNN). One problem of fusing audio and video information is that they hav... | ['Mark Hasegawa-Johnson', 'Xulin Fan', 'Zhongweiyang Xu'] | 2022-07-09 | null | null | null | null | ['speech-separation', 'speech-extraction'] | ['speech', 'speech'] | [-1.83295142e-02 -2.22213030e-01 -3.10791850e-01 -1.15869626e-01
-1.04605520e+00 -2.77352870e-01 5.10142565e-01 -2.27545630e-02
-3.45968515e-01 5.52811623e-01 4.61022705e-01 -2.19177436e-02
6.79771230e-02 -2.25623935e-01 -4.74154949e-01 -8.26301038e-01
2.02308297e-01 -2.75975466e-01 2.06822410e-01 1.71393082... | [14.399245262145996, 5.0658087730407715] |
7a936479-7ab0-496c-b2b2-adc4a784c83e | image-generation-from-scene-graphs | 1804.01622 | null | http://arxiv.org/abs/1804.01622v1 | http://arxiv.org/pdf/1804.01622v1.pdf | Image Generation from Scene Graphs | To truly understand the visual world our models should be able not only to
recognize images but also generate them. To this end, there has been exciting
recent progress on generating images from natural language descriptions. These
methods give stunning results on limited domains such as descriptions of birds
or flower... | ['Li Fei-Fei', 'Justin Johnson', 'Agrim Gupta'] | 2018-04-04 | image-generation-from-scene-graphs-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Johnson_Image_Generation_From_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Johnson_Image_Generation_From_CVPR_2018_paper.pdf | cvpr-2018-6 | ['layout-to-image-generation', 'image-generation-from-scene-graphs'] | ['computer-vision', 'computer-vision'] | [ 6.36192739e-01 5.75083256e-01 3.79223526e-01 -5.30672610e-01
-2.83658177e-01 -1.07574475e+00 9.12751853e-01 -4.72877771e-02
-3.25908698e-02 5.31296909e-01 9.51034203e-02 -3.65623742e-01
5.64146578e-01 -1.09120822e+00 -9.09207642e-01 -6.58521131e-02
1.23513445e-01 5.33712924e-01 2.62472540e-01 -2.48491615... | [11.357994079589844, -0.2392004281282425] |
6cebb484-58e8-4f02-aae1-a1b8c795a492 | training-compute-optimal-large-language | 2203.15556 | null | https://arxiv.org/abs/2203.15556v1 | https://arxiv.org/pdf/2203.15556v1.pdf | Training Compute-Optimal Large Language Models | We investigate the optimal model size and number of tokens for training a transformer language model under a given compute budget. We find that current large language models are significantly undertrained, a consequence of the recent focus on scaling language models whilst keeping the amount of training data constant. ... | ['Laurent SIfre', 'Oriol Vinyals', 'Jack W. Rae', 'Erich Elsen', 'Karen Simonyan', 'Simon Osindero', 'Aurelia Guy', 'Bogdan Damoc', 'George van den Driessche', 'Katie Millican', 'Eric Noland', 'Tom Hennigan', 'Aidan Clark', 'Johannes Welbl', 'Lisa Anne Hendricks', 'Diego de Las Casas', 'Eliza Rutherford', 'Trevor Cai',... | 2022-03-29 | null | null | null | null | ['logical-fallacy-detection', 'multi-task-language-understanding', 'moral-permissibility', 'sentence-ambiguity', 'human-organs-senses-multiple-choice', 'similarities-abstraction', 'misconceptions', 'epistemic-reasoning', 'movie-recommendation', 'known-unknowns', 'logic-grid-puzzle', 'physics-mc', 'sports-understanding'... | ['methodology', 'methodology', 'miscellaneous', 'miscellaneous', 'miscellaneous', 'miscellaneous', 'miscellaneous', 'miscellaneous', 'miscellaneous', 'miscellaneous', 'miscellaneous', 'miscellaneous', 'miscellaneous', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-... | [-5.93069732e-01 1.23002373e-01 -2.65323997e-01 -3.82368118e-01
-1.13506448e+00 -7.88675189e-01 7.42066622e-01 1.31927744e-01
-9.19888616e-01 7.71550775e-01 -2.93751596e-03 -9.48571086e-01
2.86886334e-01 -9.43051040e-01 -7.26988554e-01 -3.32000673e-01
-2.07195133e-01 8.58877122e-01 1.22009024e-01 -3.12778980... | [10.613409996032715, 8.289671897888184] |
38735e38-cb52-4ec9-aca4-61e3824438b1 | rnn-dbscan-a-density-based-clustering | null | null | https://ieeexplore.ieee.org/document/8240674 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8240674 | RNN-DBSCAN: A Density-Based Clustering Algorithm Using Reverse Nearest Neighbor Density Estimates | A new density-based clustering algorithm, RNN-DBSCAN, is presented which uses reverse nearest neighbor counts as an estimate of observation density. Clustering is performed using a DBSCAN-like approach based on k nearest neighbor graph traversals through dense observations. RNN-DBSCAN is preferable to the popular densi... | ['Krzysztof Cios', 'Avory Bryant'] | 2017-12-27 | null | null | null | null | ['3d-multi-person-pose-estimation-absolute'] | ['computer-vision'] | [-3.02572042e-01 -2.70522594e-01 -2.11708769e-01 -5.81790328e-01
-5.68050146e-01 -6.23053193e-01 6.36524856e-01 5.99484921e-01
-3.58467340e-01 5.01500964e-01 3.43050450e-01 -3.99873942e-01
-9.65486169e-01 -1.03194523e+00 -2.83260405e-01 -8.21460128e-01
-6.44436359e-01 1.29506755e+00 5.81956625e-01 2.64032245... | [7.527953624725342, 4.548065662384033] |
f666c6a3-4ab1-45b2-8ace-8fd967c89b88 | federated-stochastic-bandit-learning-with | 2303.17043 | null | https://arxiv.org/abs/2303.17043v1 | https://arxiv.org/pdf/2303.17043v1.pdf | Federated Stochastic Bandit Learning with Unobserved Context | We study the problem of federated stochastic multi-arm contextual bandits with unknown contexts, in which M agents are faced with different bandits and collaborate to learn. The communication model consists of a central server and the agents share their estimates with the central server periodically to learn to choose ... | ['Shana Moothedath', 'Jiabin Lin'] | 2023-03-29 | null | null | null | null | ['multi-armed-bandits'] | ['miscellaneous'] | [ 2.67209671e-02 -1.90574631e-01 -2.12260216e-01 -2.97966123e-01
-8.80717099e-01 -8.29505205e-01 4.92556125e-01 1.23852253e-01
-7.59211361e-01 9.67956543e-01 -1.37196099e-02 -2.43174121e-01
-5.02245128e-01 -5.97787797e-01 -9.82442856e-01 -1.10085988e+00
-2.43290514e-01 1.04477692e+00 -2.05604717e-01 1.45885020... | [4.491365909576416, 3.2491071224212646] |
afb40ed0-3d2b-4edd-9d0f-e0f8aeabbc1d | influencer-detection-with-dynamic-graph | 2211.09664 | null | https://arxiv.org/abs/2211.09664v1 | https://arxiv.org/pdf/2211.09664v1.pdf | Influencer Detection with Dynamic Graph Neural Networks | Leveraging network information for prediction tasks has become a common practice in many domains. Being an important part of targeted marketing, influencer detection can potentially benefit from incorporating dynamic network representation. In this work, we investigate different dynamic Graph Neural Networks (GNNs) con... | ['Cristián Bravo', 'Monique Snoeck', 'Bart Baesens', 'Alejandro Correa Bahnsen', 'Hernan Garcia', 'María Óskarsdóttir', 'Emiliano Penaloza', 'Elena Tiukhova'] | 2022-11-15 | null | null | null | null | ['marketing'] | ['miscellaneous'] | [ 1.08971409e-01 2.38772169e-01 -7.80688703e-01 -3.38247418e-01
-1.99647564e-02 -3.74170780e-01 9.51878965e-01 4.38531846e-01
-1.98179707e-01 4.33510751e-01 6.32923484e-01 -8.04405510e-01
-7.94424593e-01 -1.21781528e+00 -4.25408959e-01 -2.02966869e-01
-6.23449743e-01 6.42799020e-01 1.14568271e-01 -5.46593368... | [7.163846492767334, 5.974717617034912] |
64d7cffe-a640-421a-b704-08184cc339e6 | removing-distortion-effects-in-music-using | 2202.01664 | null | https://arxiv.org/abs/2202.01664v3 | https://arxiv.org/pdf/2202.01664v3.pdf | Distortion Audio Effects: Learning How to Recover the Clean Signal | Given the recent advances in music source separation and automatic mixing, removing audio effects in music tracks is a meaningful step toward developing an automated remixing system. This paper focuses on removing distortion audio effects applied to guitar tracks in music production. We explore whether effect removal c... | ['Yuki Mitsufuji', 'Yuichiro Koyama', 'Stefan Uhlich', 'Marco A. Martínez Ramírez', 'Giorgio Fabbro', 'Johannes Imort'] | 2022-02-03 | null | null | null | null | ['music-source-separation'] | ['music'] | [ 3.34601820e-01 -3.87360722e-01 1.17036723e-01 1.33289933e-01
-1.02874303e+00 -6.40093446e-01 3.40635777e-01 -1.10550016e-01
-4.32298519e-02 5.40093958e-01 6.93885803e-01 1.62485912e-01
-7.17731237e-01 -2.43433252e-01 -5.12424648e-01 -5.92443168e-01
-1.04805559e-01 3.67526822e-02 -1.85442269e-01 -2.69811898... | [15.529509544372559, 5.601144790649414] |
69dc201c-9827-4a38-bd5a-a4cf46a6a8b3 | hybrid-aco-ci-algorithm-for-beam-design | 2303.16908 | null | https://arxiv.org/abs/2303.16908v1 | https://arxiv.org/pdf/2303.16908v1.pdf | Hybrid ACO-CI Algorithm for Beam Design problems | A range of complicated real-world problems have inspired the development of several optimization methods. Here, a novel hybrid version of the Ant colony optimization (ACO) method is developed using the sample space reduction technique of the Cohort Intelligence (CI) Algorithm. The algorithm is developed, and accuracy i... | ['Aayushi Singh', 'Abhinav Anand', 'Kaustubh Dhamankar', 'Ayush Khedkar', 'Mandar S Sapre', 'Ishaan R Kale'] | 2023-03-29 | null | null | null | null | ['cantilever-beam'] | ['miscellaneous'] | [ 6.60668075e-01 1.41701594e-01 3.30170125e-01 -1.03963409e-02
-2.73887701e-02 -5.14423430e-01 1.74309045e-01 1.71570197e-01
-4.60727364e-01 1.21987820e+00 -2.10488230e-01 -1.34152293e-01
-8.10732722e-01 -9.65226829e-01 -1.33553401e-01 -8.65305245e-01
7.06327930e-02 8.06937814e-01 1.74454868e-01 -4.61800158... | [5.6993088722229, 3.4494378566741943] |
bc274042-5691-4ea3-b414-3ce665f71678 | renewable-energy-management-in-smart-home-1 | 2307.01622 | null | https://arxiv.org/abs/2307.01622v2 | https://arxiv.org/pdf/2307.01622v2.pdf | Renewable energy management in smart home environment via forecast embedded scheduling based on Recurrent Trend Predictive Neural Network | Smart home energy management systems help the distribution grid operate more efficiently and reliably, and enable effective penetration of distributed renewable energy sources. These systems rely on robust forecasting, optimization, and control/scheduling algorithms that can handle the uncertain nature of demand and re... | ['Cüneyt Güzeliş', 'Emrah Biyik', 'Onur Çopur', 'Mert Nakıp'] | 2023-07-04 | renewable-energy-management-in-smart-home | https://www.sciencedirect.com/science/article/abs/pii/S0306261923003781?dgcid=author | https://www.sciencedirect.com/science/article/abs/pii/S0306261923003781?dgcid=author | applied-energy-2023-3 | ['management', 'energy-management'] | ['miscellaneous', 'time-series'] | [-3.32828999e-01 -2.43777797e-01 -1.81754678e-01 -5.21555007e-01
-9.99505669e-02 -2.74042696e-01 4.54502285e-01 -1.51261598e-01
4.32393730e-01 1.07008541e+00 2.92626470e-01 -3.98702055e-01
-2.45513082e-01 -9.42100227e-01 7.25623071e-02 -1.23179018e+00
-2.26373568e-01 5.02859592e-01 -6.24110997e-01 -8.33470672... | [6.116786479949951, 2.7614989280700684] |
60a10bea-bddc-4e16-8d6f-f807905b37e1 | smirl-surprise-minimizing-rl-in-entropic | null | null | https://openreview.net/forum?id=H1lDbaVYvH | https://openreview.net/pdf?id=H1lDbaVYvH | SMiRL: Surprise Minimizing RL in Entropic Environments | All living organisms struggle against the forces of nature to carve out niches where
they can maintain relative stasis. We propose that such a search for order amidst
chaos might offer a unifying principle for the emergence of useful behaviors in
artificial agents. We formalize this idea into an unsupervised reinforcem... | ['Sergey Levine', 'Chelsea Finn', 'Dinesh Jayaraman', 'Coline Devin', 'Daniel Geng', 'Glen Berseth'] | 2019-09-25 | null | null | null | null | ['unsupervised-pre-training'] | ['methodology'] | [ 8.58404338e-02 2.16871783e-01 -1.04509071e-01 6.21779971e-02
2.29162332e-02 -3.28894645e-01 7.85473108e-01 1.62742525e-01
-5.55004895e-01 1.00300264e+00 -6.24399744e-02 2.63386853e-02
-3.30461234e-01 -6.25701189e-01 -6.81915879e-01 -1.11067581e+00
-5.11509299e-01 3.07777226e-01 1.81450427e-01 -7.93734670... | [3.99198842048645, 1.7679685354232788] |
88702415-28db-41f8-80a1-eb2ae0ca41b2 | provably-efficient-bayesian-optimization-with | 2306.06844 | null | https://arxiv.org/abs/2306.06844v1 | https://arxiv.org/pdf/2306.06844v1.pdf | Provably Efficient Bayesian Optimization with Unbiased Gaussian Process Hyperparameter Estimation | Gaussian process (GP) based Bayesian optimization (BO) is a powerful method for optimizing black-box functions efficiently. The practical performance and theoretical guarantees associated with this approach depend on having the correct GP hyperparameter values, which are usually unknown in advance and need to be estima... | ['Anton Van Den Hengel', 'Hongyu Zhang', 'Vu Nguyen', 'Huong Ha'] | 2023-06-12 | null | null | null | null | ['bayesian-optimization'] | ['methodology'] | [-1.15238361e-01 -1.81518629e-01 -2.32607514e-01 -1.63687587e-01
-1.31791151e+00 -4.94661719e-01 3.55047703e-01 3.01033892e-02
-3.88674021e-01 1.16315258e+00 -2.18991235e-01 -2.62616515e-01
-4.38714147e-01 -6.50963664e-01 -7.76807427e-01 -1.28261983e+00
1.26656294e-01 7.17617154e-01 2.17292644e-02 3.89870197... | [6.55449914932251, 4.035823345184326] |
954e2464-84ea-4605-a496-a3837806833f | team-ruc-aim3-technical-report-at-activitynet | 2006.07896 | null | https://arxiv.org/abs/2006.07896v1 | https://arxiv.org/pdf/2006.07896v1.pdf | Team RUC_AIM3 Technical Report at Activitynet 2020 Task 2: Exploring Sequential Events Detection for Dense Video Captioning | Detecting meaningful events in an untrimmed video is essential for dense video captioning. In this work, we propose a novel and simple model for event sequence generation and explore temporal relationships of the event sequence in the video. The proposed model omits inefficient two-stage proposal generation and directl... | ['Shi-Zhe Chen', 'Yuqing Song', 'Yida Zhao', 'Qin Jin'] | 2020-06-14 | team-ruc-aim3-technical-report-at-activitynet-2 | https://arxiv.org/abs/2006.07896 | https://arxiv.org/pdf/2006.07896.pdf | null | ['dense-captioning', 'dense-video-captioning'] | ['computer-vision', 'computer-vision'] | [ 3.30467016e-01 6.15811208e-03 -9.77770705e-03 -5.33754826e-01
-1.07832551e+00 -4.94457066e-01 7.28639960e-01 6.36853557e-03
-4.22559738e-01 7.69962072e-01 6.81001484e-01 6.56879619e-02
4.59509909e-01 -4.85858619e-01 -9.71199393e-01 -3.20998192e-01
-1.69411018e-01 4.07218367e-01 8.00484776e-01 1.37488797... | [10.438709259033203, 0.6717511415481567] |
42a0c3e0-812f-477f-b04b-ee405d6214dd | jointly-learning-to-label-sentences-and | 1811.05949 | null | http://arxiv.org/abs/1811.05949v1 | http://arxiv.org/pdf/1811.05949v1.pdf | Jointly Learning to Label Sentences and Tokens | Learning to construct text representations in end-to-end systems can be
difficult, as natural languages are highly compositional and task-specific
annotated datasets are often limited in size. Methods for directly supervising
language composition can allow us to guide the models based on existing
knowledge, regularizin... | ['Anders Søgaard', 'Marek Rei'] | 2018-11-14 | null | null | null | null | ['grammatical-error-detection'] | ['natural-language-processing'] | [ 5.36050558e-01 4.00447279e-01 -4.81780052e-01 -8.21696043e-01
-8.03381860e-01 -7.55596220e-01 6.60228133e-01 5.89972734e-01
-4.51341450e-01 6.16287529e-01 6.81432486e-01 -4.64845389e-01
4.85435963e-01 -6.38196766e-01 -8.40457737e-01 -1.01021051e-01
1.07261889e-01 5.44695079e-01 3.21786031e-02 -2.35449165... | [10.771345138549805, 8.697595596313477] |
29e808a6-0e3f-4805-889f-f2bebb413dec | how-to-use-reinforcement-learning-to | 2305.02485 | null | https://arxiv.org/abs/2305.02485v2 | https://arxiv.org/pdf/2305.02485v2.pdf | How to Use Reinforcement Learning to Facilitate Future Electricity Market Design? Part 1: A Paradigmatic Theory | In face of the pressing need of decarbonization in the power sector, the re-design of electricity market is necessary as a Marco-level approach to accommodate the high penetration of renewable generations, and to achieve power system operation security, economic efficiency, and environmental friendliness. However, exis... | ['Shiwei Xia', 'Bin Zhou', 'Ka Wing Chan', 'Siqi Bu', 'Ziqing Zhu'] | 2023-05-04 | null | null | null | null | ['philosophy'] | ['miscellaneous'] | [-8.30344677e-01 -2.40148887e-01 -3.93407196e-01 3.03431690e-01
-1.34987727e-01 -6.38823152e-01 5.40286779e-01 -2.22180307e-01
3.34486701e-02 1.17066729e+00 -3.59529555e-01 -7.11974263e-01
-4.98691618e-01 -1.15446019e+00 -4.63968795e-03 -8.07400882e-01
-1.72790080e-01 2.23186135e-01 -3.19490522e-01 -4.45792317... | [5.628102779388428, 2.568023920059204] |
0e25fdc0-9701-4275-a0a6-a8503ee77951 | toward-accurate-and-reliable-iris | 2110.10334 | null | https://arxiv.org/abs/2110.10334v2 | https://arxiv.org/pdf/2110.10334v2.pdf | Toward Accurate and Reliable Iris Segmentation Using Uncertainty Learning | Iris segmentation is a deterministic part of the iris recognition system. Unreliable segmentation of iris regions especially the limbic area is still the bottleneck problem, which impedes more accurate recognition. To make further efforts on accurate and reliable iris segmentation, we propose a bilateral self-attention... | ['Zhenan Sun', 'Ran He', 'Min Ren', 'Yunlong Wang', 'Muyi Sun', 'Huaibo Huang', 'Jianze Wei'] | 2021-10-20 | null | null | null | null | ['iris-segmentation'] | ['medical'] | [ 1.13060340e-01 9.99407768e-02 -2.94953048e-01 -6.55295312e-01
-6.11050844e-01 -1.21523619e-01 1.76821530e-01 -1.12892777e-01
-2.50157148e-01 4.11538929e-01 5.57180524e-01 -8.24093670e-02
-4.31923926e-01 -4.99977469e-01 -6.19941115e-01 -8.45545411e-01
3.29476863e-01 2.33555213e-01 5.91818914e-02 2.73224175... | [3.8468551635742188, -3.5623302459716797] |
dea2bc93-2db5-4f86-82b7-03f73fabf771 | learning-graph-normalization-for-graph-neural | 2009.11746 | null | https://arxiv.org/abs/2009.11746v1 | https://arxiv.org/pdf/2009.11746v1.pdf | Learning Graph Normalization for Graph Neural Networks | Graph Neural Networks (GNNs) have attracted considerable attention and have emerged as a new promising paradigm to process graph-structured data. GNNs are usually stacked to multiple layers and the node representations in each layer are computed through propagating and aggregating the neighboring node features with res... | ['Chun-Guang Li', 'Xin Tang', 'Yihao Chen', 'Xianbiao Qi', 'Rong Xiao'] | 2020-09-24 | null | https://openreview.net/forum?id=oLltLS5F9R | https://openreview.net/pdf?id=oLltLS5F9R | null | ['graph-regression'] | ['graphs'] | [ 1.32319376e-01 3.94736975e-02 -3.16879869e-01 -5.52592039e-01
2.11484864e-01 -2.61942685e-01 5.11513650e-01 6.14893913e-01
-3.89707774e-01 4.21840131e-01 9.82247218e-02 -2.34571859e-01
-3.97952706e-01 -1.22093284e+00 -6.05225563e-01 -6.96036935e-01
-1.79404318e-01 2.85436243e-01 1.64717898e-01 -4.23618048... | [7.212252140045166, 6.244455337524414] |
8339b16b-eabd-4cea-8843-f0d1a1bfc51c | large-language-models-are-implicitly-topic | 2301.11916 | null | https://arxiv.org/abs/2301.11916v2 | https://arxiv.org/pdf/2301.11916v2.pdf | Large Language Models Are Implicitly Topic Models: Explaining and Finding Good Demonstrations for In-Context Learning | In recent years, pre-trained large language models have demonstrated remarkable efficiency in achieving an inference-time few-shot learning capability known as in-context learning. However, existing literature has highlighted the sensitivity of this capability to the selection of few-shot demonstrations. The underlying... | ['Mark Steyvers', 'Michael Saxon', 'William Yang Wang', 'Wanrong Zhu', 'Xinyi Wang'] | 2023-01-27 | null | null | null | null | ['topic-models'] | ['natural-language-processing'] | [ 2.87069321e-01 2.54845828e-01 -5.69182277e-01 -4.57163781e-01
-1.09743953e+00 -1.75680742e-01 1.10585332e+00 1.62082776e-01
-6.48822427e-01 6.50581360e-01 2.61745781e-01 -2.71807045e-01
-4.32546102e-02 -4.54080254e-01 -7.47833073e-01 -4.15580988e-01
-1.15613937e-01 5.89120507e-01 1.43779650e-01 1.78822666... | [10.724669456481934, 8.118325233459473] |
da24a793-9701-4062-86c8-95fc7554ae6f | online-heart-rate-prediction-using | 1807.04667 | null | http://arxiv.org/abs/1807.04667v1 | http://arxiv.org/pdf/1807.04667v1.pdf | Online Heart Rate Prediction using Acceleration from a Wrist Worn Wearable | In this paper we study the prediction of heart rate from acceleration using a
wrist worn wearable. Although existing photoplethysmography (PPG) heart rate
sensors provide reliable measurements, they use considerably more energy than
accelerometers and have a major impact on battery life of wearable devices. By
using en... | ['Raul Santos-Rodriguez', 'Ryan McConville', 'Gareth Archer', 'Robert Piechocki', 'Ian Craddock', 'Herman ter Horst', 'James Pope'] | 2018-06-25 | null | null | null | null | ['photoplethysmography-ppg'] | ['medical'] | [ 3.29390556e-01 1.98265746e-01 -2.57092237e-01 -2.52593666e-01
-3.09237897e-01 -1.69483453e-01 -1.69382930e-01 4.37672406e-01
-3.26770574e-01 7.82476306e-01 1.56577095e-01 -3.16470653e-01
2.51264095e-01 -8.72267127e-01 -2.71147072e-01 -3.92029136e-01
-3.35395128e-01 1.30799487e-01 -9.79344770e-02 2.28769019... | [13.931632041931152, 3.0333139896392822] |
39486a34-2c0a-463b-a640-c5f0f3875644 | self-distillation-for-gaussian-process | 2304.02641 | null | https://arxiv.org/abs/2304.02641v1 | https://arxiv.org/pdf/2304.02641v1.pdf | Self-Distillation for Gaussian Process Regression and Classification | We propose two approaches to extend the notion of knowledge distillation to Gaussian Process Regression (GPR) and Gaussian Process Classification (GPC); data-centric and distribution-centric. The data-centric approach resembles most current distillation techniques for machine learning, and refits a model on determinist... | ['Lars Nørvang Andersen', 'Kenneth Borup'] | 2023-04-05 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [-2.70274356e-02 2.71485835e-01 -9.29248556e-02 -3.23873997e-01
-7.99723029e-01 -3.34851265e-01 9.79176044e-01 2.20215112e-01
-3.34881455e-01 7.16026723e-01 -2.65957832e-01 -6.50712967e-01
-5.53658009e-01 -9.12959695e-01 -6.44581556e-01 -9.79478717e-01
6.07689656e-02 1.07089186e+00 3.04425240e-01 2.86062121... | [7.128129005432129, 3.823024034500122] |
44165090-2ccb-4b92-8705-277d78dfc47c | end-to-end-neural-networks-for-subvocal | null | null | https://www.semanticscholar.org/paper/End-to-end-neural-networks-for-subvocal-speech-Rosello-Toman/6676c8ce28ee02e785a1f1136112beee94a5c7e1 | https://web.stanford.edu/class/cs224s/project/reports_2017/Pol_Rosello.pdf | End-to-end neural networks for subvocal speech recognition | Subvocalization is a phenomenon observed while subjects read or think, characterized by involuntary facial and laryngeal muscle movements. By measuring this muscle activity using surface electromyography (EMG), it may be possible to perform automatic speech recognition (ASR) and enable silent, handsfree human-computer ... | ['Nipun Agarwala', 'Pamela Toman', 'Pol Rosello'] | 2017-06-11 | null | null | null | cs-224s-2017-6 | ['electromyography-emg'] | ['medical'] | [ 7.05768645e-01 2.13866875e-01 -4.60909218e-01 -2.43089601e-01
-1.18817878e+00 -3.68660778e-01 5.88649154e-01 -7.54068255e-01
-5.91485739e-01 7.20525503e-01 5.04367769e-01 -2.84299910e-01
5.63786775e-02 1.82534605e-01 -5.24973452e-01 -6.18940771e-01
-1.33307710e-01 8.73416364e-02 -3.45597267e-01 -1.12369932... | [14.902728080749512, 6.021342754364014] |
39a47255-3f8b-4392-b9c7-f060dd2459c5 | a-versatile-deep-learning-based-protein | 2307.01066 | null | https://arxiv.org/abs/2307.01066v1 | https://arxiv.org/pdf/2307.01066v1.pdf | A versatile deep learning-based protein-ligand interaction prediction model for accurate binding affinity scoring and virtual screening | Protein--ligand interaction (PLI) prediction is critical in drug discovery, aiding the identification and enhancement of molecules that effectively bind to target proteins. Despite recent advances in deep learning-based PLI prediction, developing a versatile model capable of accurate binding affinity scoring and virtua... | ['Woo Youn Kim', 'Jaechang Lim', 'Sang-Yeon Hwang', 'Seokhyun Moon'] | 2023-07-03 | null | null | null | null | ['drug-discovery'] | ['medical'] | [ 3.73269945e-01 -3.19998920e-01 -2.29008257e-01 -1.32744342e-01
-8.99767160e-01 -6.63457215e-01 5.34561336e-01 6.40320480e-01
-4.92930502e-01 1.36990249e+00 -1.47563905e-01 -6.22541726e-01
-2.32295826e-01 -4.71384674e-01 -8.00287604e-01 -9.67056811e-01
-1.70709670e-01 9.35449064e-01 1.90305382e-01 -3.28369588... | [4.9267578125, 5.60942268371582] |
778fa43a-5fff-4aac-a849-cd76ad2f7d5a | batchgnn-efficient-cpu-based-distributed-gnn | 2306.13814 | null | https://arxiv.org/abs/2306.13814v1 | https://arxiv.org/pdf/2306.13814v1.pdf | BatchGNN: Efficient CPU-Based Distributed GNN Training on Very Large Graphs | We present BatchGNN, a distributed CPU system that showcases techniques that can be used to efficiently train GNNs on terabyte-sized graphs. It reduces communication overhead with macrobatching in which multiple minibatches' subgraph sampling and feature fetching are batched into one communication relay to reduce redun... | ['Bo Wu', 'Ke Ding', 'Rita Brugarolas Brufau', 'Loc Hoang'] | 2023-06-23 | null | null | null | null | ['graph-partitioning'] | ['graphs'] | [-2.52078325e-01 3.49031985e-01 -2.73795635e-01 -3.86340618e-01
-5.33959329e-01 -4.66971636e-01 2.17438042e-01 9.27371234e-02
-3.42242032e-01 6.72774613e-01 -2.95552880e-01 -8.25097024e-01
-7.76877403e-02 -1.65116501e+00 -6.55825913e-01 -4.89529014e-01
-5.63919902e-01 9.77727890e-01 3.96793991e-01 -1.17963098... | [6.96732759475708, 5.753192901611328] |
815618d8-f3c6-4420-9cce-8e8d5250f6c0 | spatio-temporal-tendency-reasoning-for-human | 2210.03659 | null | https://arxiv.org/abs/2210.03659v2 | https://arxiv.org/pdf/2210.03659v2.pdf | Spatio-temporal Tendency Reasoning for Human Body Pose and Shape Estimation from Videos | In this paper, we present a spatio-temporal tendency reasoning (STR) network for recovering human body pose and shape from videos. Previous approaches have focused on how to extend 3D human datasets and temporal-based learning to promote accuracy and temporal smoothing. Different from them, our STR aims to learn accura... | ['Lei Lin', 'Pan Li', 'Kehua Ma', 'Hu Cao', 'Suping Wu', 'Boyang Zhang'] | 2022-10-07 | null | null | null | null | ['temporal-sequences'] | ['reasoning'] | [-2.62280941e-01 -2.50627220e-01 -2.64454305e-01 -2.14897722e-01
-2.64812350e-01 -1.16680097e-02 2.07128868e-01 -3.26160133e-01
-3.36471945e-01 2.90247798e-01 7.20007539e-01 2.19902843e-01
-3.57519060e-01 -5.50583541e-01 -6.99470520e-01 -5.54108977e-01
-5.82427561e-01 -2.01443732e-01 4.30263370e-01 -2.39985287... | [7.3710150718688965, -0.2944898307323456] |
0b629eb7-3aac-46fa-b83b-a2089029365e | learning-multi-modal-class-specific-tokens | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Xu_Learning_Multi-Modal_Class-Specific_Tokens_for_Weakly_Supervised_Dense_Object_Localization_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Xu_Learning_Multi-Modal_Class-Specific_Tokens_for_Weakly_Supervised_Dense_Object_Localization_CVPR_2023_paper.pdf | Learning Multi-Modal Class-Specific Tokens for Weakly Supervised Dense Object Localization | Weakly supervised dense object localization (WSDOL) relies generally on Class Activation Mapping (CAM), which exploits the correlation between the class weights of the image classifier and the pixel-level features. Due to the limited ability to address intra-class variations, the image classifier cannot properly as... | ['Dan Xu', 'Farid Boussaid', 'Mohammed Bennamoun', 'Wanli Ouyang', 'Lian Xu'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['object-localization', 'weakly-supervised-semantic-segmentation'] | ['computer-vision', 'computer-vision'] | [ 2.88000941e-01 -9.53681991e-02 -5.29135764e-01 -5.99864304e-01
-9.91540790e-01 -6.63505912e-01 5.78096747e-01 2.74088681e-01
-5.85060596e-01 5.03084719e-01 -1.34985358e-01 2.12490410e-01
1.71119452e-01 -5.82754612e-01 -8.43499720e-01 -1.00209785e+00
4.01366055e-01 2.21201703e-01 2.71978348e-01 1.70042440... | [9.57353401184082, 0.9755120873451233] |
f6d21799-703c-4d73-910f-7168f88db8e0 | dual-objective-fine-tuning-of-bert-for-entity | null | null | https://doi.org/10.14778/3467861.3467878 | http://vldb.org/pvldb/vol14/p1913-peeters.pdf | Dual-Objective Fine-Tuning of BERT for Entity Matching | An increasing number of data providers have adopted shared numbering schemes such as GTIN, ISBN, DUNS, or ORCID numbers for identifying entities in the respective domain. This means for data integration that shared identifiers are often available for a subset of the entity descriptions to be integrated while such ident... | ['Christian Bizer', 'Ralph Peeters'] | 2021-06-01 | null | null | null | proceedings-of-the-vldb-endowment-2021-6 | ['data-integration', 'entity-resolution'] | ['knowledge-base', 'natural-language-processing'] | [ 4.58095782e-02 2.61299223e-01 -6.23783827e-01 -6.63044989e-01
-1.03339791e+00 -7.61352718e-01 6.42747462e-01 6.04271591e-01
-4.19602126e-01 4.52818424e-01 5.41332271e-03 -2.33854562e-01
-3.21536392e-01 -9.42421854e-01 -7.50947058e-01 -2.01843575e-01
1.70853302e-01 1.06590915e+00 5.11768498e-02 -4.08638507... | [9.520976066589355, 8.5457763671875] |
59c10008-a7d2-46cb-9918-291af3890040 | data-augmentation-for-recommender-system-a | 2306.13050 | null | https://arxiv.org/abs/2306.13050v1 | https://arxiv.org/pdf/2306.13050v1.pdf | Data augmentation for recommender system: A semi-supervised approach using maximum margin matrix factorization | Collaborative filtering (CF) has become a popular method for developing recommender systems (RS) where ratings of a user for new items is predicted based on her past preferences and available preference information of other users. Despite the popularity of CF-based methods, their performance is often greatly limited by... | ['Arun K Pujari', 'Vikas Kumar', 'Venkateswara Rao Kagita', 'Shamal Shaikh'] | 2023-06-22 | null | null | null | null | ['collaborative-filtering'] | ['miscellaneous'] | [ 2.88111210e-01 4.46548797e-02 -5.87656558e-01 -5.51299691e-01
-3.30784917e-01 -4.98715281e-01 4.16930705e-01 2.49198928e-01
-1.06952481e-01 7.52269089e-01 5.50456703e-01 -3.69960129e-01
-2.26218641e-01 -6.83966279e-01 -3.42337251e-01 -4.68358725e-01
5.28308712e-02 4.00105864e-01 -2.29755174e-02 -5.14775753... | [10.016509056091309, 5.6433000564575195] |
79193c13-1207-4e28-a5e5-0cc6eee84b1c | photometric-mesh-optimization-for-video | 1903.08642 | null | http://arxiv.org/abs/1903.08642v1 | http://arxiv.org/pdf/1903.08642v1.pdf | Photometric Mesh Optimization for Video-Aligned 3D Object Reconstruction | In this paper, we address the problem of 3D object mesh reconstruction from
RGB videos. Our approach combines the best of multi-view geometric and
data-driven methods for 3D reconstruction by optimizing object meshes for
multi-view photometric consistency while constraining mesh deformations with a
shape prior. We pose... | ['Matthew Fisher', 'Chen-Hsuan Lin', 'Bryan C. Russell', 'Eli Shechtman', 'Vladimir G. Kim', 'Simon Lucey', 'Oliver Wang'] | 2019-03-20 | photometric-mesh-optimization-for-video-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Lin_Photometric_Mesh_Optimization_for_Video-Aligned_3D_Object_Reconstruction_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Lin_Photometric_Mesh_Optimization_for_Video-Aligned_3D_Object_Reconstruction_CVPR_2019_paper.pdf | cvpr-2019-6 | ['3d-object-reconstruction'] | ['computer-vision'] | [ 3.80121589e-01 1.99547142e-01 5.10161638e-01 -3.58584613e-01
-7.60641694e-01 -7.94620752e-01 4.26817924e-01 -2.87934661e-01
-1.48536041e-01 3.76614779e-01 -1.35349914e-01 -1.89455599e-02
2.43083581e-01 -9.03355420e-01 -1.03435814e+00 -3.47093970e-01
5.17580032e-01 8.97017121e-01 3.97953361e-01 -1.82053462... | [9.099369049072266, -2.9651849269866943] |
b39ad97e-fb5a-4318-b365-8fff6e6661cd | the-devil-is-in-the-details-a-diagnostic | 2105.05332 | null | https://arxiv.org/abs/2105.05332v2 | https://arxiv.org/pdf/2105.05332v2.pdf | The DEVIL is in the Details: A Diagnostic Evaluation Benchmark for Video Inpainting | Quantitative evaluation has increased dramatically among recent video inpainting work, but the video and mask content used to gauge performance has received relatively little attention. Although attributes such as camera and background scene motion inherently change the difficulty of the task and affect methods differe... | ['Jason J. Corso', 'Ryan Szeto'] | 2021-05-11 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Szeto_The_DEVIL_Is_in_the_Details_A_Diagnostic_Evaluation_Benchmark_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Szeto_The_DEVIL_Is_in_the_Details_A_Diagnostic_Evaluation_Benchmark_CVPR_2022_paper.pdf | cvpr-2022-1 | ['video-inpainting'] | ['computer-vision'] | [ 2.84565806e-01 -6.90258622e-01 -3.10456961e-01 -9.65942666e-02
-7.84242749e-01 -9.02824998e-01 7.03948021e-01 7.10335597e-02
-1.28250852e-01 5.11153817e-01 4.47275192e-01 -4.38516550e-02
-1.14510782e-01 -4.38839704e-01 -7.22165942e-01 -4.73679692e-01
-2.50396997e-01 5.26141338e-02 1.61756709e-01 -1.50862828... | [10.908031463623047, -0.6821495294570923] |
3435afd8-13d1-40cf-bbb4-681b75017382 | dropout-sampling-for-robust-object-detection | 1710.06677 | null | http://arxiv.org/abs/1710.06677v2 | http://arxiv.org/pdf/1710.06677v2.pdf | Dropout Sampling for Robust Object Detection in Open-Set Conditions | Dropout Variational Inference, or Dropout Sampling, has been recently
proposed as an approximation technique for Bayesian Deep Learning and evaluated
for image classification and regression tasks. This paper investigates the
utility of Dropout Sampling for object detection for the first time. We
demonstrate how label u... | ['Niko Sünderhauf', 'Lachlan Nicholson', 'Dimity Miller', 'Feras Dayoub'] | 2017-10-18 | null | null | null | null | ['robust-object-detection'] | ['computer-vision'] | [ 2.38446712e-01 1.65636614e-01 -9.11606997e-02 -3.36613059e-01
-1.13389087e+00 -2.95631498e-01 5.52020788e-01 -2.01441094e-01
-9.72154796e-01 7.42506206e-01 -5.80752969e-01 -5.28115369e-02
1.12000301e-01 -3.55456889e-01 -1.05834305e+00 -7.31974244e-01
-9.24485445e-04 4.88161623e-01 6.07056797e-01 6.12759650... | [8.765830993652344, 1.308901309967041] |
5209801e-0783-417b-83c9-964573f0b034 | global-ecg-classification-by-self-operational | 2204.03768 | null | https://arxiv.org/abs/2204.03768v2 | https://arxiv.org/pdf/2204.03768v2.pdf | Global ECG Classification by Self-Operational Neural Networks with Feature Injection | Objective: Global (inter-patient) ECG classification for arrhythmia detection over Electrocardiogram (ECG) signal is a challenging task for both humans and machines. The main reason is the significant variations of both normal and arrhythmic ECG patterns among patients. Automating this process with utmost accuracy is, ... | ['Moncef Gabbouj', 'Serkan Kiranyaz', 'Muhammad Uzair Zahid'] | 2022-04-07 | null | null | null | null | ['arrhythmia-detection', 'ecg-classification'] | ['medical', 'medical'] | [ 0.2759255 -0.12362531 0.04840629 -0.2540696 -0.71276814 -0.6250859
-0.0934353 0.54698133 -0.21650136 0.65274197 -0.29989657 -0.21467927
-0.51435786 -0.53026146 -0.25536343 -0.59494513 -0.6173565 0.24438597
-0.26980498 0.23664582 0.11892931 0.7128665 -1.1359605 0.43468365
0.8567408 1.4903955 -0.1... | [14.290853500366211, 3.2692625522613525] |
c667b2ca-0f96-4a34-afef-a62390ff27e4 | maskbev-joint-object-detection-and-footprint | 2307.01864 | null | https://arxiv.org/abs/2307.01864v1 | https://arxiv.org/pdf/2307.01864v1.pdf | MaskBEV: Joint Object Detection and Footprint Completion for Bird's-eye View 3D Point Clouds | Recent works in object detection in LiDAR point clouds mostly focus on predicting bounding boxes around objects. This prediction is commonly achieved using anchor-based or anchor-free detectors that predict bounding boxes, requiring significant explicit prior knowledge about the objects to work properly. To remedy thes... | ['Philippe Giguère', 'François Pomerleau', 'Jean-Michel Fortin', 'William Guimont-Martin'] | 2023-07-04 | null | null | null | null | ['object-detection'] | ['computer-vision'] | [ 3.79240997e-02 4.80838232e-02 -6.54283836e-02 -6.64547741e-01
-3.49101394e-01 -5.28064132e-01 5.70495605e-01 1.86374828e-01
-3.30895394e-01 2.61365473e-01 -2.28247732e-01 -2.57137567e-01
6.80162385e-02 -9.74742055e-01 -9.22599733e-01 -1.46000117e-01
-3.62882065e-03 9.59696591e-01 1.00994992e+00 2.76979003... | [7.696545600891113, -2.7766900062561035] |
80de6dbb-98ff-4320-9c04-3c31b1127f0d | learning-new-tasks-from-a-few-examples-with | 2210.17437 | null | https://arxiv.org/abs/2210.17437v2 | https://arxiv.org/pdf/2210.17437v2.pdf | Learning New Tasks from a Few Examples with Soft-Label Prototypes | It has been experimentally demonstrated that humans are able to learn in a manner that allows them to make predictions on categories for which they have not seen any examples (Malaviya et al., 2022). Sucholutsky and Schonlau (2020) have recently presented a machine learning approach that aims to do the same. They utili... | ['Helen Yannakoudakis', 'Ekaterina Shutova', 'Avyav Kumar Singh'] | 2022-10-31 | null | null | null | null | ['one-shot-learning'] | ['methodology'] | [ 5.46548247e-01 4.19792026e-01 -2.72738904e-01 -5.97168088e-01
-7.33909547e-01 -5.51421106e-01 9.93756294e-01 9.87254530e-02
-5.78248501e-01 8.75657439e-01 -1.01366967e-01 -1.39314130e-01
-2.82720178e-01 -7.82818019e-01 -3.70170534e-01 -4.20053482e-01
-5.93276359e-02 7.97472477e-01 4.73509967e-01 -7.29791299... | [9.943037986755371, 3.043727397918701] |
16fa46d2-92a4-4e54-8eab-4e702e1a8ca9 | chipformer-transferable-chip-placement-via | 2306.14744 | null | https://arxiv.org/abs/2306.14744v1 | https://arxiv.org/pdf/2306.14744v1.pdf | ChiPFormer: Transferable Chip Placement via Offline Decision Transformer | Placement is a critical step in modern chip design, aiming to determine the positions of circuit modules on the chip canvas. Recent works have shown that reinforcement learning (RL) can improve human performance in chip placement. However, such an RL-based approach suffers from long training time and low transfer abili... | ['Ping Luo', 'Jianye Hao', 'Bin Wang', 'Zhentao Tang', 'Jinxin Liu', 'Yao Lai'] | 2023-06-26 | null | null | null | null | ['offline-rl'] | ['playing-games'] | [-2.04087675e-01 9.04622674e-02 -5.55979609e-01 -1.69518813e-01
-1.25424778e+00 -9.07773256e-01 -3.09629381e-01 -1.84100464e-01
8.36825222e-02 9.05335844e-01 -2.06637263e-01 -6.08103037e-01
-8.85751918e-02 -6.00717485e-01 -1.11310196e+00 -6.24278545e-01
1.59733787e-01 6.53286040e-01 -1.23921759e-01 2.42017329... | [5.763621807098389, 3.1492345333099365] |
bbe38e70-da62-440b-8d19-61ff595574bf | stereo-video-reconstruction-without-explicit | 2109.08227 | null | https://arxiv.org/abs/2109.08227v1 | https://arxiv.org/pdf/2109.08227v1.pdf | Stereo Video Reconstruction Without Explicit Depth Maps for Endoscopic Surgery | We introduce the task of stereo video reconstruction or, equivalently, 2D-to-3D video conversion for minimally invasive surgical video. We design and implement a series of end-to-end U-Net-based solutions for this task by varying the input (single frame vs. multiple consecutive frames), loss function (MSE, MAE, or perc... | ['Eric Oermann', 'Doug Kondziolka', 'Kyunghyun Cho', 'Jesse Swanson', 'Annika Brundyn'] | 2021-09-16 | null | null | null | null | ['video-reconstruction'] | ['computer-vision'] | [ 1.99875116e-01 4.62776005e-01 -2.95593739e-01 -1.00592658e-01
-8.42684269e-01 -6.03911221e-01 2.95300096e-01 -2.40484834e-01
-8.57368469e-01 4.84236032e-01 5.38023949e-01 -4.79500473e-01
-3.28951269e-01 -3.52063477e-01 -8.26739490e-01 -6.46066248e-01
-1.39702037e-01 -2.62054384e-01 2.90119229e-03 -6.16302304... | [14.013263702392578, -3.2926766872406006] |
3e133533-e529-4260-9419-f10333f445d1 | learning-transparent-object-matting | 1907.11544 | null | https://arxiv.org/abs/1907.11544v1 | https://arxiv.org/pdf/1907.11544v1.pdf | Learning Transparent Object Matting | This paper addresses the problem of image matting for transparent objects. Existing approaches often require tedious capturing procedures and long processing time, which limit their practical use. In this paper, we formulate transparent object matting as a refractive flow estimation problem, and propose a deep learning... | ['Guan-Ying Chen', 'Kwan-Yee K. Wong', 'Kai Han'] | 2019-07-25 | null | null | null | null | ['transparent-objects'] | ['computer-vision'] | [ 4.15491670e-01 -9.30654705e-02 4.89082068e-01 -4.06122267e-01
-5.46695530e-01 -2.32576251e-01 9.66138914e-02 -5.30141652e-01
-2.62947917e-01 6.33802056e-01 -4.38151300e-01 -2.40062371e-01
4.59689260e-01 -1.00369406e+00 -1.12778759e+00 -6.23717070e-01
2.45612696e-01 3.31814706e-01 3.50498527e-01 1.38165966... | [10.52667236328125, -1.0986382961273193] |
fa5d3861-5e7c-43ac-b27b-41603a45c288 | a-dynamic-reduction-network-for-point-clouds | 2003.08013 | null | https://arxiv.org/abs/2003.08013v1 | https://arxiv.org/pdf/2003.08013v1.pdf | A Dynamic Reduction Network for Point Clouds | Classifying whole images is a classic problem in machine learning, and graph neural networks are a powerful methodology to learn highly irregular geometries. It is often the case that certain parts of a point cloud are more important than others when determining overall classification. On graph structures this started ... | ['Thomas Klijnsma', 'Shamik Ghosh', 'Lindsey Gray'] | 2020-03-18 | null | null | null | null | ['superpixel-image-classification'] | ['computer-vision'] | [-3.79704009e-03 2.30482906e-01 -1.01185031e-01 -3.99146169e-01
-1.20166801e-01 -5.74914336e-01 6.68608725e-01 8.72608662e-01
-4.21950281e-01 3.71754408e-01 -1.08694255e-01 -3.45746100e-01
-4.64693815e-01 -1.30366695e+00 -7.35861540e-01 -6.81939125e-01
-4.94843185e-01 5.27036786e-01 6.09361947e-01 -2.86027163... | [7.07744836807251, 5.955507278442383] |
19b460d5-0eda-4d67-8eb4-9a9a30623ce0 | entity-extraction-with-knowledge-from-web | 1911.09373 | null | https://arxiv.org/abs/1911.09373v1 | https://arxiv.org/pdf/1911.09373v1.pdf | Entity Extraction with Knowledge from Web Scale Corpora | Entity extraction is an important task in text mining and natural language processing. A popular method for entity extraction is by comparing substrings from free text against a dictionary of entities. In this paper, we present several techniques as a post-processing step for improving the effectiveness of the existing... | ['Zeyu Huang', 'Rui Zhang', 'Zeyi Wen'] | 2019-11-21 | null | null | null | null | ['entity-extraction'] | ['natural-language-processing'] | [ 5.12649827e-02 2.00533211e-01 -4.19722229e-01 -3.56334895e-01
-7.13354349e-01 -6.98091269e-01 6.76023126e-01 7.83061147e-01
-1.02949941e+00 1.13684726e+00 2.18358055e-01 -3.85690898e-01
6.26770407e-02 -1.10834932e+00 -2.38083169e-01 -4.61432710e-02
-2.99789816e-01 4.03042048e-01 7.21350729e-01 -3.06094795... | [9.402563095092773, 8.975397109985352] |
2948a276-dc5e-42ce-869c-5dc6bb9ff664 | on-the-noise-sensitivity-of-the-randomized | 2305.17435 | null | https://arxiv.org/abs/2305.17435v2 | https://arxiv.org/pdf/2305.17435v2.pdf | On the Noise Sensitivity of the Randomized SVD | The randomized singular value decomposition (R-SVD) is a popular sketching-based algorithm for efficiently computing the partial SVD of a large matrix. When the matrix is low-rank, the R-SVD produces its partial SVD exactly; but when the rank is large, it only yields an approximation. Motivated by applications in data ... | ['Elad Romanov'] | 2023-05-27 | null | null | null | null | ['dimensionality-reduction'] | ['methodology'] | [ 4.68061984e-01 1.54535184e-02 1.57913655e-01 4.53392982e-01
-1.08058214e+00 -8.59050870e-01 4.77112055e-01 -3.68449122e-01
-6.59509376e-02 3.53744835e-01 6.36059880e-01 -4.35010970e-01
-4.89386708e-01 -3.96289527e-01 -5.51674724e-01 -1.14038539e+00
-4.50837612e-01 -1.19899940e-02 -2.81201810e-01 -2.52605051... | [6.987048625946045, 4.561107635498047] |
4c4c1cfe-9c39-4dae-a86f-47e5c0e98796 | c-1-loss-learn-to-classify-c-classes-of | null | null | https://openreview.net/forum?id=6kruvdT0yfY | https://openreview.net/pdf?id=6kruvdT0yfY | C+1 Loss: Learn to Classify C Classes of Interest and the Background Class Differentially | There is one kind of problem all around the classification area, where we want to classify C+1 classes of samples, including C semantically deterministic classes which we call classes of interest and the (C+1)th semantically undeterministic class which we call background class. In spite of most classification algorithm... | ['ShiLiang Pu', 'Jun Che', 'Yi Lu', 'Mengyu Ye', 'Xile Shen', 'Changhuai Chen'] | 2021-09-29 | null | null | null | null | ['human-parsing'] | ['computer-vision'] | [ 5.23390055e-01 4.79202509e-01 -2.32247770e-01 -6.45659804e-01
-3.87652338e-01 -4.55089927e-01 4.93507266e-01 3.83631498e-01
-5.89174032e-01 6.07048869e-01 -3.32575023e-01 -3.40779454e-01
-1.81633368e-01 -9.15888369e-01 -6.70233130e-01 -8.46114099e-01
-9.87125188e-02 5.20084262e-01 6.34241045e-01 6.32891525... | [9.077954292297363, 3.9825148582458496] |
fea3392c-e00b-4ee5-be3e-a0e0865dcb57 | better-quality-estimation-for-low-resource | 2203.08259 | null | https://arxiv.org/abs/2203.08259v1 | https://arxiv.org/pdf/2203.08259v1.pdf | Better Quality Estimation for Low Resource Corpus Mining | Quality Estimation (QE) models have the potential to change how we evaluate and maybe even train machine translation models. However, these models still lack the robustness to achieve general adoption. We show that State-of-the-art QE models, when tested in a Parallel Corpus Mining (PCM) setting, perform unexpectedly b... | ['Derry Wijaya', 'Jiho Lee', 'Muhammed Yusuf Kocyigit'] | 2022-03-15 | null | https://aclanthology.org/2022.findings-acl.45 | https://aclanthology.org/2022.findings-acl.45.pdf | findings-acl-2022-5 | ['parallel-corpus-mining'] | ['natural-language-processing'] | [ 1.07177302e-01 -2.39322662e-01 8.48508924e-02 -2.50542998e-01
-1.84970284e+00 -7.87049711e-01 6.51609421e-01 1.38757110e-01
-9.22186196e-01 1.08076406e+00 1.62644491e-01 -6.32517695e-01
1.79242820e-01 -3.99847239e-01 -1.24680924e+00 -1.65978223e-01
9.23102051e-02 8.44079077e-01 3.81255835e-01 -6.50088668... | [11.606793403625488, 10.29140853881836] |
fde20a44-1fc4-4a0a-9ab7-fd86f2f80f8e | unsupervised-model-personalization-while | 2003.13296 | null | https://arxiv.org/abs/2003.13296v1 | https://arxiv.org/pdf/2003.13296v1.pdf | Unsupervised Model Personalization while Preserving Privacy and Scalability: An Open Problem | This work investigates the task of unsupervised model personalization, adapted to continually evolving, unlabeled local user images. We consider the practical scenario where a high capacity server interacts with a myriad of resource-limited edge devices, imposing strong requirements on scalability and local data privac... | ['Gregory Slabaugh', 'Matthias De Lange', 'Xu Jia', 'Tinne Tuytelaars', 'Ales Leonardis', 'Sarah Parisot'] | 2020-03-30 | unsupervised-model-personalization-while-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/De_Lange_Unsupervised_Model_Personalization_While_Preserving_Privacy_and_Scalability_An_Open_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/De_Lange_Unsupervised_Model_Personalization_While_Preserving_Privacy_and_Scalability_An_Open_CVPR_2020_paper.pdf | cvpr-2020-6 | ['scene-recognition'] | ['computer-vision'] | [ 4.66928631e-01 2.69515323e-03 -1.95826322e-01 -5.78001380e-01
-6.90237224e-01 -7.60466635e-01 3.79860222e-01 -5.52444793e-02
-8.88900638e-01 6.87813938e-01 6.89756945e-02 -3.43510032e-01
-7.40184262e-02 -3.54178131e-01 -7.73045540e-01 -9.10225928e-01
1.60723329e-01 3.95418257e-01 1.52589470e-01 2.01014161... | [10.350964546203613, 3.233020067214966] |
56d4c21b-5c09-475d-812a-cc3a88a71fd7 | a-unified-approach-to-lane-change-intention | 2304.13732 | null | https://arxiv.org/abs/2304.13732v1 | https://arxiv.org/pdf/2304.13732v1.pdf | A Unified Approach to Lane Change Intention Recognition and Driving Status Prediction through TCN-LSTM and Multi-Task Learning Models | Lane change (LC) is a continuous and complex operation process. Accurately detecting and predicting LC processes can help traffic participants better understand their surrounding environment, recognize potential LC safety hazards, and improve traffic safety. This present paper focuses on LC processes, developing an LC ... | ['Qiaojun Xiang', 'Ou Zheng', 'Xin Gu', 'Mohamed Abdel-Aty', 'Renteng Yuan'] | 2023-04-25 | null | null | null | null | ['intent-detection'] | ['natural-language-processing'] | [ 7.78702721e-02 -6.13801599e-01 -6.20637655e-01 -3.96769404e-01
-5.01825690e-01 3.30715105e-02 7.75563598e-01 -2.62681901e-01
-4.84002203e-01 5.81051171e-01 1.80365685e-02 -8.39701116e-01
-1.63755447e-01 -6.83646858e-01 -5.79333961e-01 -7.02260733e-01
-1.35175452e-01 1.35598183e-01 3.69119227e-01 -1.96088538... | [6.1016845703125, 1.0295873880386353] |
6451baf4-f323-4512-ac4d-6d2f422c3b76 | bidirectional-transition-based-dependency | null | null | https://aaai.org/ojs/index.php/AAAI/article/view/4733 | https://aaai.org/ojs/index.php/AAAI/article/view/4733/4611 | Bidirectional Transition-Based Dependency Parsing | Transition-based dependency parsing is a fast and effective approach for dependency parsing. Traditionally, a transitionbased dependency parser processes an input sentence and predicts a sequence of parsing actions in a left-to-right manner. During this process, an early prediction error may negatively impact the predi... | ['Kewei Tu', 'Yunzhe Yuan', 'Yong Jiang'] | 2019-07-17 | null | null | null | aaai-2019-7 | ['transition-based-dependency-parsing'] | ['natural-language-processing'] | [ 2.91932732e-01 2.38501921e-01 -3.23487371e-01 -8.57330918e-01
-1.40465343e+00 -8.04887056e-01 1.79546893e-01 2.29041148e-02
-2.94034004e-01 6.70386493e-01 3.42589676e-01 -8.81493390e-01
4.15431529e-01 -6.10558033e-01 -7.62600899e-01 -4.14401829e-01
1.65911451e-01 4.69290197e-01 6.11311316e-01 -2.76796432... | [10.382502555847168, 9.578158378601074] |
8e5cecf1-e0ba-4911-b583-709c7e753d10 | classifying-temporal-relations-with-rich | null | null | https://aclanthology.org/N13-1112 | https://aclanthology.org/N13-1112.pdf | Classifying Temporal Relations with Rich Linguistic Knowledge | null | ["Jennifer D{'}Souza", 'Vincent Ng'] | 2013-06-01 | null | null | null | naacl-2013-6 | ['temporal-information-extraction'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.437550067901611, 3.6100759506225586] |
a4c954fe-a5cb-4907-841a-aa7c27d7126e | learning-representation-over-dynamic-graph | 2106.01678 | null | https://arxiv.org/abs/2106.01678v1 | https://arxiv.org/pdf/2106.01678v1.pdf | Learning Representation over Dynamic Graph using Aggregation-Diffusion Mechanism | Representation learning on graphs that evolve has recently received significant attention due to its wide application scenarios, such as bioinformatics, knowledge graphs, and social networks. The propagation of information in graphs is important in learning dynamic graph representations, and most of the existing method... | ['Zhongjie Wang', 'Xiaofei Xu', 'Zhiying Tu', 'Mingyi Liu'] | 2021-06-03 | null | null | null | null | ['dynamic-link-prediction'] | ['graphs'] | [-3.73964272e-02 1.96504071e-01 -4.83960152e-01 -1.08212747e-01
1.58221424e-01 -4.47564840e-01 7.66192794e-01 8.22673023e-01
-1.31785750e-01 7.00753987e-01 3.57931077e-01 -2.39576682e-01
-1.99190065e-01 -1.37302005e+00 -5.07773459e-01 -6.05389237e-01
-4.59998488e-01 3.13701183e-01 7.06347525e-01 -1.66394711... | [7.192470550537109, 6.0567731857299805] |
fe7289fe-c9df-4e1e-9fd4-aae9dcfba86c | interactive-instance-based-evaluation-of | null | null | https://aclanthology.org/D18-2020 | https://aclanthology.org/D18-2020.pdf | Interactive Instance-based Evaluation of Knowledge Base Question Answering | Most approaches to Knowledge Base Question Answering are based on semantic parsing. In this paper, we present a tool that aids in debugging of question answering systems that construct a structured semantic representation for the input question. Previous work has largely focused on building question answering interface... | ['Iryna Gurevych', 'Daniil Sorokin'] | 2018-11-01 | null | null | null | emnlp-2018-11 | ['knowledge-base-question-answering'] | ['natural-language-processing'] | [-1.85738981e-01 7.12838173e-01 1.33789301e-01 -7.71575570e-01
-7.79799402e-01 -7.84768045e-01 1.45445079e-01 5.39402485e-01
2.51966178e-01 4.34671044e-01 3.69192883e-02 -9.46743846e-01
-3.57735783e-01 -6.90365255e-01 -5.70128441e-01 6.49190187e-01
3.91377985e-01 6.05729818e-01 9.84184742e-01 -5.34356833... | [10.861194610595703, 7.876500606536865] |
bed841a0-8fdb-4c5c-af13-7b4f003dfb2b | hegel-hypergraph-transformer-for-long | 2210.04126 | null | https://arxiv.org/abs/2210.04126v1 | https://arxiv.org/pdf/2210.04126v1.pdf | HEGEL: Hypergraph Transformer for Long Document Summarization | Extractive summarization for long documents is challenging due to the extended structured input context. The long-distance sentence dependency hinders cross-sentence relations modeling, the critical step of extractive summarization. This paper proposes HEGEL, a hypergraph neural network for long document summarization ... | ['Jiawei Zhang', 'Xiao Liu', 'Haopeng Zhang'] | 2022-10-09 | null | null | null | null | ['extractive-summarization'] | ['natural-language-processing'] | [ 2.88457841e-01 6.05554044e-01 -4.99802232e-01 -2.02632129e-01
-7.25829065e-01 -5.04034460e-01 4.69944984e-01 5.50535083e-01
-2.75965661e-01 9.07957077e-01 1.34697676e+00 -4.08144481e-02
-3.28621536e-01 -6.75585091e-01 -7.15797126e-01 -2.34516054e-01
-1.69141397e-01 6.11693144e-01 7.50026479e-02 -4.39194441... | [12.557394027709961, 9.509763717651367] |
cd60709e-9099-4c21-81cd-ce23f529e9ff | causal-inference-using-linear-time-varying | 2012.13025 | null | https://arxiv.org/abs/2012.13025v3 | https://arxiv.org/pdf/2012.13025v3.pdf | Causal Inference from Slowly Varying Nonstationary Processes | Causal inference from observational data following the restricted structural causal model (SCM) framework hinges largely on the asymmetry between cause and effect from the data generating mechanisms, such as non-Gaussianity or nonlinearity. This methodology can be adapted to stationary time series, yet inferring causal... | ['Yu Xiang', 'Kang Du'] | 2020-12-23 | null | null | null | null | ['causal-identification'] | ['reasoning'] | [ 3.93811375e-01 -4.54286933e-01 -2.36987203e-01 -2.17437625e-01
-1.68479919e-01 -7.39746392e-01 8.39496851e-01 -1.31381169e-01
1.93519697e-01 1.02775347e+00 3.68838251e-01 -5.46760440e-01
-9.51350033e-01 -8.53639245e-01 -7.07819521e-01 -9.07904267e-01
-6.85531139e-01 6.63466156e-02 1.18828759e-01 2.48193353... | [7.696591377258301, 5.1565632820129395] |
078331c5-1836-4e66-a30d-6c8bd1db5077 | exploring-automated-essay-scoring-for | 1706.03335 | null | http://arxiv.org/abs/1706.03335v3 | http://arxiv.org/pdf/1706.03335v3.pdf | Exploring Automated Essay Scoring for Nonnative English Speakers | Automated Essay Scoring (AES) has been quite popular and is being widely
used. However, lack of appropriate methodology for rating nonnative English
speakers' essays has meant a lopsided advancement in this field. In this paper,
we report initial results of our experiments with nonnative AES that learns
from manual eva... | ['Amber Nigam'] | 2017-06-11 | null | null | null | null | ['automated-essay-scoring'] | ['natural-language-processing'] | [-3.23988289e-01 -1.23913743e-01 2.48078272e-01 -7.00986207e-01
-8.23487341e-01 -9.90142822e-01 4.65286344e-01 2.28289574e-01
-4.89054233e-01 1.11356556e+00 2.14427799e-01 -3.81492317e-01
-1.79733094e-02 -5.19492924e-01 -7.73635134e-02 -1.04497507e-01
3.14262390e-01 3.37465435e-01 7.47014135e-02 -4.77354676... | [11.298182487487793, 9.315217018127441] |
41fcefeb-117f-4776-96d2-5c8373a59720 | counterfactual-explanations-for-models-of | 2111.05711 | null | https://arxiv.org/abs/2111.05711v1 | https://arxiv.org/pdf/2111.05711v1.pdf | Counterfactual Explanations for Models of Code | Machine learning (ML) models play an increasingly prevalent role in many software engineering tasks. However, because most models are now powered by opaque deep neural networks, it can be difficult for developers to understand why the model came to a certain conclusion and how to act upon the model's prediction. Motiva... | ['Satish Chandra', 'Vijayaraghavan Murali', 'Isil Dillig', 'Jürgen Cito'] | 2021-11-10 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [ 1.91504553e-01 1.00961888e+00 -2.73788780e-01 -3.56823921e-01
-2.64507025e-01 -4.90387648e-01 7.08891988e-01 1.55977398e-01
1.11766562e-01 7.46137679e-01 2.17718601e-01 -1.23382044e+00
1.10904686e-01 -5.97038269e-01 -1.17248416e+00 1.20276898e-01
-3.04954369e-02 -1.33876186e-02 -4.49820654e-03 -2.16239113... | [8.740116119384766, 5.674536228179932] |
6f1e42cd-bab5-47ad-bb0a-d0ec00e65daf | real-time-bearing-fault-diagnosis-based-on | 2304.09100 | null | https://arxiv.org/abs/2304.09100v1 | https://arxiv.org/pdf/2304.09100v1.pdf | Real Time Bearing Fault Diagnosis Based on Convolutional Neural Network and STM32 Microcontroller | With the rapid development of big data and edge computing, many researchers focus on improving the accuracy of bearing fault classification using deep learning models, and implementing the deep learning classification model on limited resource platforms such as STM32. To this end, this paper realizes the identification... | ['Wenhao Liao'] | 2023-04-14 | null | null | null | null | ['edge-computing'] | ['time-series'] | [-4.83800203e-01 -5.10138154e-01 3.54932427e-01 6.98782653e-02
1.56606480e-01 5.19026935e-01 -3.59585971e-01 -5.80821872e-01
-2.84433693e-01 1.12046257e-01 -6.08697236e-01 -5.99527359e-01
2.14513745e-02 -8.94315481e-01 -1.59170568e-01 -4.99460638e-01
-2.09507734e-01 2.61720885e-02 2.25806177e-01 -8.13132748... | [6.962789058685303, 2.3042685985565186] |
97910397-c069-441c-9858-1ad58fce4c5f | reload-reinforcement-learning-with-optimistic | 2302.01275 | null | https://arxiv.org/abs/2302.01275v2 | https://arxiv.org/pdf/2302.01275v2.pdf | ReLOAD: Reinforcement Learning with Optimistic Ascent-Descent for Last-Iterate Convergence in Constrained MDPs | In recent years, Reinforcement Learning (RL) has been applied to real-world problems with increasing success. Such applications often require to put constraints on the agent's behavior. Existing algorithms for constrained RL (CRL) rely on gradient descent-ascent, but this approach comes with a caveat. While these algor... | ['Tom Zahavy', 'Satinder Singh', 'Sebastian Flennerhag', 'Vivek Veeriah', "Brendan O'Donoghue", 'Ted Moskovitz'] | 2023-02-02 | null | null | null | null | ['continuous-control'] | ['playing-games'] | [-6.51695952e-02 -3.15130353e-02 -5.97142756e-01 1.20985880e-01
-8.03546607e-01 -5.53844869e-01 4.24470484e-01 3.98506761e-01
-7.48111367e-01 1.30467498e+00 -1.01534881e-01 -4.43128675e-01
-2.37826273e-01 -5.80823720e-01 -5.43552577e-01 -8.50011528e-01
-3.64752889e-01 5.87897778e-01 -1.13707930e-01 -3.04443926... | [4.243557453155518, 2.5494112968444824] |
f089251c-db67-4c6e-a8cc-323f81dc8671 | medical-image-synthesis-for-data-augmentation | 1807.10225 | null | http://arxiv.org/abs/1807.10225v2 | http://arxiv.org/pdf/1807.10225v2.pdf | Medical Image Synthesis for Data Augmentation and Anonymization using Generative Adversarial Networks | Data diversity is critical to success when training deep learning models.
Medical imaging data sets are often imbalanced as pathologic findings are
generally rare, which introduces significant challenges when training deep
learning models. In this work, we propose a method to generate synthetic
abnormal MRI images with... | ['Neil A. Tenenholtz', 'Katherine Andriole', 'Hoo-chang Shin', 'Matthew L Senjem', 'Mark Michalski', 'Jameson K Rogers', 'Jeffrey L Gunter', 'Christopher G Schwarz'] | 2018-07-26 | null | null | null | null | ['medical-image-generation'] | ['medical'] | [ 5.11561334e-01 7.13493407e-01 -8.24492946e-02 -6.91785455e-01
-9.72391009e-01 -3.91363084e-01 3.81207407e-01 -5.22233136e-02
-5.91665983e-01 1.04502594e+00 3.54187936e-01 -4.29555386e-01
1.81545302e-01 -6.98731184e-01 -9.38202262e-01 -5.40216565e-01
-1.28288314e-01 7.61567712e-01 -4.04210120e-01 5.17515913... | [14.306623458862305, -1.9306249618530273] |
a70297b1-74f1-4ee3-b93f-90871eb6ef90 | listen-only-to-me-how-well-can-target-speech | 2204.04811 | null | https://arxiv.org/abs/2204.04811v2 | https://arxiv.org/pdf/2204.04811v2.pdf | Listen only to me! How well can target speech extraction handle false alarms? | Target speech extraction (TSE) extracts the speech of a target speaker in a mixture given auxiliary clues characterizing the speaker, such as an enrollment utterance. TSE addresses thus the challenging problem of simultaneously performing separation and speaker identification. There has been much progress in extraction... | ['Tomohiro Nakatani', 'Hiroshi Sato', 'Katerina Zmolikova', 'Tsubasa Ochiai', 'Keisuke Kinoshita', 'Marc Delcroix'] | 2022-04-11 | null | null | null | null | ['speech-extraction', 'speaker-identification'] | ['speech', 'speech'] | [ 5.49844384e-01 2.14693785e-01 -2.35356335e-02 -1.07887775e-01
-1.03969896e+00 -5.60634851e-01 4.90676343e-01 -3.08435019e-02
-2.90402532e-01 3.75907451e-01 1.31383151e-01 -5.23633122e-01
1.95184037e-01 -1.54753104e-01 -2.13779315e-01 -8.76948953e-01
4.82537523e-02 2.73218095e-01 -2.99736168e-02 -8.80684853... | [14.685436248779297, 5.979491710662842] |
1e09b40a-fe03-492c-a709-e3f6e0d8651f | exploiting-rich-syntactic-information-for | 1808.07624 | null | http://arxiv.org/abs/1808.07624v1 | http://arxiv.org/pdf/1808.07624v1.pdf | Exploiting Rich Syntactic Information for Semantic Parsing with Graph-to-Sequence Model | Existing neural semantic parsers mainly utilize a sequence encoder, i.e., a
sequential LSTM, to extract word order features while neglecting other valuable
syntactic information such as dependency graph or constituent trees. In this
paper, we first propose to use the \textit{syntactic graph} to represent three
types of... | ['Li-Wei Chen', 'Lingfei Wu', 'Kun Xu', 'Vadim Sheinin', 'Mo Yu', 'Zhiguo Wang'] | 2018-08-23 | exploiting-rich-syntactic-information-for-1 | https://aclanthology.org/D18-1110 | https://aclanthology.org/D18-1110.pdf | emnlp-2018-10 | ['graph-to-sequence'] | ['natural-language-processing'] | [ 1.46944389e-01 2.19953641e-01 -2.09008574e-01 -7.61361420e-01
-3.85475725e-01 -6.55128360e-01 1.93012178e-01 1.20063908e-01
-5.62444687e-01 6.61401272e-01 1.88928902e-01 -7.54371166e-01
3.83018285e-01 -1.02772331e+00 -9.52890515e-01 -3.57103318e-01
2.55879927e-02 4.14161012e-02 4.09397185e-01 -2.58639336... | [10.453052520751953, 9.402749061584473] |
242efaa8-70c9-464f-9403-2c1361b80887 | knowledge-embedded-representation-learning | 1807.00505 | null | http://arxiv.org/abs/1807.00505v1 | http://arxiv.org/pdf/1807.00505v1.pdf | Knowledge-Embedded Representation Learning for Fine-Grained Image Recognition | Humans can naturally understand an image in depth with the aid of rich
knowledge accumulated from daily lives or professions. For example, to achieve
fine-grained image recognition (e.g., categorizing hundreds of subordinate
categories of birds) usually requires a comprehensive visual concept
organization including cat... | ['Liang Lin', 'Yang Wu', 'Xiaonan Luo', 'Tianshui Chen', 'Riquan Chen'] | 2018-07-02 | null | null | null | null | ['fine-grained-image-recognition'] | ['computer-vision'] | [ 9.89936441e-02 4.44886871e-02 -2.28481010e-01 -5.78327179e-01
7.10275676e-03 -6.94720447e-01 5.85929930e-01 3.32519382e-01
-1.13457277e-01 4.24914718e-01 1.33253381e-01 -1.44064039e-01
-6.18463933e-01 -1.03381813e+00 -7.58356988e-01 -7.49838054e-01
1.14297643e-01 1.96800753e-01 1.53348178e-01 -1.50473252... | [9.639163970947266, 1.999297022819519] |
00182bf1-2b93-4015-9ecf-71263808d4fe | eeg-emotion-recognition-using-dynamical-graph | null | null | https://doi.org/10.1109/taffc.2018.2817622 | https://pdfs.semanticscholar.org/3378/0d6c82a0c060a9a35fd07effbd2fbb3e5b82.pdf?_ga=2.118713866.2007402716.1567967863-1098133910.1548150455 | EEG emotion recognition using dynamical graph convolutional neural networks | In this paper, a multichannel EEG emotion recognition method based on a novel dynamical graph convolutional neural networks (DGCNN) is proposed. The basic idea of the proposed EEG emotion recognition method is to use a graph to model the multichannel EEG features and then perform EEG emotion classification based on thi... | ['Peng Song', 'Zhen Cui', 'Wenming Zheng', 'Zhenyang Zhang'] | 2018-03-21 | null | null | null | ieee-transactions-on-affective-computing-2018 | ['eeg-emotion-recognition'] | ['miscellaneous'] | [-3.40964757e-02 -2.21303850e-01 3.43294889e-01 -4.31698322e-01
1.29790679e-01 2.25373600e-02 4.34463508e-02 3.38786505e-02
-5.01672864e-01 8.73061419e-01 -2.05491453e-01 1.88082114e-01
-3.62056464e-01 -6.39972210e-01 -2.98550874e-01 -8.46609950e-01
-6.08076096e-01 -1.81311384e-01 -3.67826551e-01 -2.01403305... | [13.10566520690918, 3.475336790084839] |
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