paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
f3d2eac9-6e37-4d58-9f57-38de713d9f42 | ddg-da-data-distribution-generation-for | 2201.04038 | null | https://arxiv.org/abs/2201.04038v2 | https://arxiv.org/pdf/2201.04038v2.pdf | DDG-DA: Data Distribution Generation for Predictable Concept Drift Adaptation | In many real-world scenarios, we often deal with streaming data that is sequentially collected over time. Due to the non-stationary nature of the environment, the streaming data distribution may change in unpredictable ways, which is known as concept drift. To handle concept drift, previous methods first detect when/wh... | ['Jiang Bian', 'Yingce Xia', 'Weiqing Liu', 'Xiao Yang', 'Wendi Li'] | 2022-01-11 | null | null | null | null | ['stock-trend-prediction', 'stock-market-prediction', 'stock-prediction'] | ['time-series', 'time-series', 'time-series'] | [ 1.36145324e-01 -6.04221761e-01 -6.05143793e-02 -5.72619498e-01
7.73812234e-02 -6.61529243e-01 3.58571768e-01 3.63066822e-01
-8.82828534e-02 7.00359762e-01 1.83605209e-01 -1.22624718e-01
6.99577332e-02 -9.23959315e-01 -7.23320782e-01 -8.21991682e-01
-2.31467411e-01 2.98094153e-01 2.61853635e-01 -3.25231194... | [7.37805700302124, 2.9896442890167236] |
0ec3254e-1838-46c5-9b3a-9a6127330e00 | relational-deep-feature-learning-for | 2003.00697 | null | https://arxiv.org/abs/2003.00697v3 | https://arxiv.org/pdf/2003.00697v3.pdf | Relational Deep Feature Learning for Heterogeneous Face Recognition | Heterogeneous Face Recognition (HFR) is a task that matches faces across two different domains such as visible light (VIS), near-infrared (NIR), or the sketch domain. Due to the lack of databases, HFR methods usually exploit the pre-trained features on a large-scale visual database that contain general facial informati... | ['Ig-Jae Kim', 'Taeoh Kim', 'Kyungjae Lee', 'Sangyoun Lee', 'MyeongAh Cho'] | 2020-03-02 | null | null | null | null | ['heterogeneous-face-recognition'] | ['computer-vision'] | [ 1.29533917e-01 -5.97146526e-02 -2.55362123e-01 -5.74065745e-01
-4.96371120e-01 -1.05609760e-01 5.38168669e-01 -4.21466172e-01
3.54894958e-02 1.90176010e-01 1.81260154e-01 9.97711271e-02
-1.24596924e-01 -1.04397845e+00 -6.90036535e-01 -5.98287106e-01
1.31517932e-01 1.03184037e-01 -3.40200998e-02 -4.25355852... | [13.114914894104004, 0.5339385271072388] |
db530c71-ee7f-4275-9c20-508c6d1b67cd | one-class-one-click-quasi-scene-level-weakly | 2211.12657 | null | https://arxiv.org/abs/2211.12657v1 | https://arxiv.org/pdf/2211.12657v1.pdf | One Class One Click: Quasi Scene-level Weakly Supervised Point Cloud Semantic Segmentation with Active Learning | Reliance on vast annotations to achieve leading performance severely restricts the practicality of large-scale point cloud semantic segmentation. For the purpose of reducing data annotation costs, effective labeling schemes are developed and contribute to attaining competitive results under weak supervision strategy. R... | ['Jie Shao', 'Wei Yao', 'Puzuo Wang'] | 2022-11-23 | null | null | null | null | ['scene-classification'] | ['computer-vision'] | [ 5.24019003e-01 3.23642969e-01 -6.57799363e-01 -5.90232074e-01
-1.46301305e+00 -5.48645496e-01 5.41877568e-01 3.10798347e-01
-2.95968324e-01 5.97066462e-01 -3.22150469e-01 -1.67951643e-01
-2.35878468e-01 -7.88971305e-01 -8.56641650e-01 -7.65379190e-01
4.98752259e-02 4.92175281e-01 5.23477852e-01 1.42245442... | [8.04211711883545, -3.074188232421875] |
da93b289-1668-4ecc-8f83-357ec768acaf | reactive-multi-stage-feature-fusion-for | 1908.05067 | null | https://arxiv.org/abs/1908.05067v1 | https://arxiv.org/pdf/1908.05067v1.pdf | Reactive Multi-Stage Feature Fusion for Multimodal Dialogue Modeling | Visual question answering and visual dialogue tasks have been increasingly studied in the multimodal field towards more practical real-world scenarios. A more challenging task, audio visual scene-aware dialogue (AVSD), is proposed to further advance the technologies that connect audio, vision, and language, which intro... | ['Yun-Nung Chen', 'Shang-Yu Su', 'Yu-Hsuan Deng', 'Hsiao-Hua Cheng', 'Tzu-Chuan Lin', 'Yi-Ting Yeh'] | 2019-08-14 | null | null | null | null | ['scene-aware-dialogue'] | ['computer-vision'] | [-2.23191485e-01 -3.84116113e-01 1.95462957e-01 -2.92284161e-01
-6.11787558e-01 -4.01483625e-01 8.36557806e-01 1.08638391e-01
-4.64019835e-01 4.21115696e-01 5.96623838e-01 -7.41994753e-02
2.99783528e-01 -2.35310346e-01 -1.47092909e-01 -4.70263332e-01
1.41834065e-01 1.66740134e-01 4.91348982e-01 -4.55116481... | [10.801175117492676, 1.26587975025177] |
52b56c21-69b3-4870-a39d-02d1baeb2f3d | escort-ethereum-smart-contracts-vulnerability | 2103.12607 | null | https://arxiv.org/abs/2103.12607v1 | https://arxiv.org/pdf/2103.12607v1.pdf | ESCORT: Ethereum Smart COntRacTs Vulnerability Detection using Deep Neural Network and Transfer Learning | Ethereum smart contracts are automated decentralized applications on the blockchain that describe the terms of the agreement between buyers and sellers, reducing the need for trusted intermediaries and arbitration. However, the deployment of smart contracts introduces new attack vectors into the cryptocurrency systems.... | ['Farinaz Koushanfar', 'Ahmad Reza Sadeghi', 'Alexandra Dmitrienko', 'Christoph Sendner', 'Hossein Fereidooni', 'Huili Chen', 'Oliver Lutz'] | 2021-03-23 | null | null | null | null | ['vulnerability-detection'] | ['miscellaneous'] | [-2.26948425e-01 1.81287527e-01 -4.02629912e-01 -9.56133828e-02
-9.31893945e-01 -1.28060222e+00 6.03819549e-01 -1.30214468e-01
-2.00973958e-01 3.89814317e-01 -2.76598427e-02 -1.18949163e+00
1.72418192e-01 -9.66363907e-01 -6.97867036e-01 -6.59586132e-01
-4.49460477e-01 5.63595593e-01 2.76610494e-01 -4.38362151... | [6.816624641418457, 7.321304798126221] |
fcb7f4f6-3278-43b9-ab6a-c06ec9d3bc0b | is-neural-topic-modelling-better-than | null | null | https://openreview.net/forum?id=UBk6b94uH7 | https://openreview.net/pdf?id=UBk6b94uH7 | Is Neural Topic Modelling Better than Clustering? An Empirical Study on Clustering with Contextual Embeddings for Topics | Recent work incorporates pre-trained word embeddings such as BERT embeddings into Neural Topic Models (NTMs), generating highly coherent topics. However, with high-quality contextualized document representations, do we really need sophisticated neural models to obtain coherent and interpretable topics? In this paper, w... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['topic-models'] | ['natural-language-processing'] | [-3.35898638e-01 3.90660822e-01 -4.33734298e-01 -5.67848802e-01
-8.56884301e-01 -1.25996217e-01 8.71446013e-01 1.86862871e-01
-4.04851079e-01 7.71306336e-01 9.31549251e-01 -3.01651359e-01
9.02688503e-02 -9.55953360e-01 -4.46060330e-01 -4.62824643e-01
-9.26251262e-02 5.66750228e-01 5.28435819e-02 -2.34903440... | [10.517633438110352, 7.101612567901611] |
f2abf253-0cdc-4f29-98b6-9e09c9064ea4 | a-methodology-for-search-space-reduction-in | 1809.07045 | null | http://arxiv.org/abs/1809.07045v2 | http://arxiv.org/pdf/1809.07045v2.pdf | A Methodology for Search Space Reduction in QoS Aware Semantic Web Service Composition | The semantic information regulates the expressiveness of a web service.
State-of-the-art approaches in web services research have used the semantics of
a web service for different purposes, mainly for service discovery,
composition, execution etc. In this paper, our main focus is on semantic driven
Quality of Service (... | ['Soumi Chattopadhyay', 'Ansuman Banerjee'] | 2018-09-19 | null | null | null | null | ['service-composition'] | ['miscellaneous'] | [ 5.91741269e-03 -3.14710736e-01 1.61541283e-01 -5.98500371e-01
-3.36185336e-01 -5.44871509e-01 5.14943540e-01 -2.02144638e-01
3.52767818e-02 2.18137756e-01 4.21559900e-01 -1.31770417e-01
-4.91203606e-01 -1.10888755e+00 7.40475729e-02 -8.08860600e-01
1.66176885e-01 5.11462629e-01 7.18037367e-01 -4.64355648... | [8.611181259155273, 6.954575538635254] |
5c1a013a-0228-4187-a242-8b6243988707 | multi-goal-multi-agent-path-finding-via | 2009.05161 | null | https://arxiv.org/abs/2009.05161v1 | https://arxiv.org/pdf/2009.05161v1.pdf | Multi-Goal Multi-Agent Path Finding via Decoupled and Integrated Goal Vertex Ordering | We introduce multi-goal multi agent path finding (MAPF$^{MG}$) which generalizes the standard discrete multi-agent path finding (MAPF) problem. While the task in MAPF is to navigate agents in an undirected graph from their starting vertices to one individual goal vertex per agent, MAPF$^{MG}$ assigns each agent multipl... | ['Pavel Surynek'] | 2020-09-10 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [ 8.70463476e-02 3.52750719e-01 4.54231128e-02 7.70383552e-02
-5.52029550e-01 -7.10846066e-01 5.24896204e-01 5.98440230e-01
-5.36834955e-01 1.18834901e+00 -3.96363765e-01 -4.66352582e-01
-1.03174162e+00 -1.47488201e+00 -4.71406341e-01 -5.30610561e-01
-7.94276595e-01 1.17906606e+00 7.30193496e-01 -5.87342203... | [4.9755024909973145, 1.7805142402648926] |
48acd86c-4e72-4e5c-bed7-6c0f8c59cbc1 | study-of-proximal-normalized-subband-adaptive | 2108.10219 | null | https://arxiv.org/abs/2108.10219v1 | https://arxiv.org/pdf/2108.10219v1.pdf | Study of Proximal Normalized Subband Adaptive Algorithm for Acoustic Echo Cancellation | In this paper, we propose a novel normalized subband adaptive filter algorithm suited for sparse scenarios, which combines the proportionate and sparsity-aware mechanisms. The proposed algorithm is derived based on the proximal forward-backward splitting and the soft-thresholding methods. We analyze the mean and mean s... | ['Qiangming Cai', 'Lu Lu', 'Zongsheng Zheng', 'Rodrigo C. de Lamare', 'Yi Yu', 'Gang Guo'] | 2021-08-14 | null | null | null | null | ['acoustic-echo-cancellation', 'acoustic-echo-cancellation'] | ['medical', 'speech'] | [ 5.71409941e-01 -8.78384039e-02 2.87980467e-01 -4.03544128e-01
-7.07957208e-01 -2.90260524e-01 2.99170315e-01 -6.17388412e-02
-4.76900309e-01 5.77925622e-01 3.32585782e-01 -1.80097193e-01
-6.78209245e-01 -2.13720828e-01 -2.59662986e-01 -1.16985464e+00
-6.07358292e-02 -1.92723379e-01 -2.90424824e-02 -4.27889898... | [15.091806411743164, 5.730691432952881] |
033dfa78-fa8f-49eb-9b04-11f6853c8be8 | a-survey-on-organoid-image-analysis-platforms | 2301.02341 | null | https://arxiv.org/abs/2301.02341v1 | https://arxiv.org/pdf/2301.02341v1.pdf | A survey on Organoid Image Analysis Platforms | An in-vitro cell culture system is used for biological discoveries and hypothesis-driven research on a particular cell type to understand mechanistic or test pharmaceutical drugs. Conventional in-vitro cultures have been applied to primary cells and immortalised cell lines plated on 2D surfaces. However, they are unrel... | ['Azadeh Nazemi', 'Alireza Ranjbaran'] | 2023-01-06 | null | null | null | null | ['culture'] | ['speech'] | [-1.94072127e-01 -1.59154847e-01 -9.48107019e-02 7.70419836e-01
-2.15936661e-01 -7.92587638e-01 5.01531243e-01 6.63442492e-01
-2.78632134e-01 7.96216309e-01 8.61494392e-02 -3.06683183e-01
3.69467378e-01 -3.72196585e-01 -4.15879369e-01 -9.06792164e-01
-3.16105746e-02 5.65623701e-01 4.73237157e-01 1.83588918... | [13.860261917114258, -3.088029623031616] |
a1cf74f8-11f6-4ca2-8d0b-54c7c3450c1b | open-world-semi-supervised-learning-1 | 2102.03526 | null | https://arxiv.org/abs/2102.03526v3 | https://arxiv.org/pdf/2102.03526v3.pdf | Open-World Semi-Supervised Learning | A fundamental limitation of applying semi-supervised learning in real-world settings is the assumption that unlabeled test data contains only classes previously encountered in the labeled training data. However, this assumption rarely holds for data in-the-wild, where instances belonging to novel classes may appear at ... | ['Jure Leskovec', 'Maria Brbic', 'Kaidi Cao'] | 2021-02-06 | open-world-semi-supervised-learning | https://openreview.net/forum?id=6VhmvP7XZue | https://openreview.net/pdf?id=6VhmvP7XZue | iclr-2022-4 | ['open-world-semi-supervised-learning'] | ['computer-vision'] | [ 6.29183769e-01 4.09501046e-01 -2.99888998e-01 -6.41630888e-01
-8.73536110e-01 -7.05383480e-01 3.52518022e-01 4.22582120e-01
-6.29527152e-01 9.53064919e-01 -2.94658780e-01 -2.21883491e-01
1.14672624e-01 -6.43901706e-01 -8.91414225e-01 -1.03489411e+00
9.21573043e-02 9.25905287e-01 2.19617262e-01 3.87315065... | [9.541582107543945, 3.356410026550293] |
935135f4-5ed7-467d-9990-1140bd63990f | symbolic-regression-via-control-variable | 2306.08057 | null | https://arxiv.org/abs/2306.08057v1 | https://arxiv.org/pdf/2306.08057v1.pdf | Symbolic Regression via Control Variable Genetic Programming | Learning symbolic expressions directly from experiment data is a vital step in AI-driven scientific discovery. Nevertheless, state-of-the-art approaches are limited to learning simple expressions. Regressing expressions involving many independent variables still remain out of reach. Motivated by the control variable ex... | ['Yexiang Xue', 'Nan Jiang'] | 2023-05-25 | null | null | null | null | ['symbolic-regression'] | ['knowledge-base'] | [ 4.10300374e-01 1.09470934e-01 -5.19258022e-01 -5.06565332e-01
-7.10294545e-01 -7.07694471e-01 2.00816333e-01 2.71622449e-01
-3.24680686e-01 1.34682703e+00 -5.89774191e-01 -4.58403975e-01
-1.58565804e-01 -8.88730943e-01 -1.14923429e+00 -6.24548972e-01
-5.25706112e-01 9.45602596e-01 -3.20934914e-02 -2.44845331... | [8.51333236694336, 6.973209857940674] |
2f1df4f1-2cb8-44b9-8151-47038f7e363a | dias-a-domain-independent-alife-based-problem | 2203.06855 | null | https://arxiv.org/abs/2203.06855v2 | https://arxiv.org/pdf/2203.06855v2.pdf | DIAS: A Domain-Independent Alife-Based Problem-Solving System | A domain-independent problem-solving system based on principles of Artificial Life is introduced. In this system, DIAS, the input and output dimensions of the domain are laid out in a spatial medium. A population of actors, each seeing only part of this medium, solves problems collectively in it. The process is indepen... | ['Risto Miikkulainen', 'Hormoz Shahrzad', 'Babak Hodjat'] | 2022-03-14 | null | null | null | null | ['artificial-life'] | ['miscellaneous'] | [-3.29347670e-01 3.41397703e-01 2.98771650e-01 2.24890172e-01
-2.89978832e-01 -8.12896848e-01 6.52629077e-01 8.76437202e-02
-2.33375296e-01 9.65628564e-01 1.15674123e-01 2.93931216e-02
-8.35596263e-01 -1.06680846e+00 -2.48624712e-01 -8.49254191e-01
-2.77614623e-01 1.18532908e+00 1.53771684e-01 -3.18087935... | [4.111660957336426, 1.6595423221588135] |
988cecbe-44fd-4ce5-a0bb-f0d8768135f2 | deep-residual-network-based-food-recognition | 2005.04292 | null | https://arxiv.org/abs/2005.04292v2 | https://arxiv.org/pdf/2005.04292v2.pdf | Deep Residual Network based food recognition for enhanced Augmented Reality application | Deep neural network based learning approaches is widely utilized for image classification or object detection based problems with remarkable outcomes. Realtime Object state estimation of objects can be used to track and estimate the features that the object of the current frame possesses without causing any significant... | ['Sainath G', 'Siddarth S', 'Vignesh S'] | 2020-05-08 | null | null | null | null | ['food-recognition'] | ['computer-vision'] | [ 7.52537251e-02 -3.13416839e-01 -2.74163157e-01 -3.71433228e-01
9.19916183e-02 -3.01667005e-01 1.98038995e-01 3.06457818e-01
-3.60377818e-01 3.42976749e-01 -1.63697332e-01 7.24709928e-02
-6.67831972e-02 -9.15439367e-01 -4.74636167e-01 -5.56769729e-01
-1.00041099e-01 2.32017726e-01 9.77984816e-02 -2.57643580... | [8.331857681274414, -0.9234289526939392] |
39ea574f-33f6-40c0-9f24-209de88c437f | remips-physically-consistent-3d | null | null | http://proceedings.neurips.cc/paper/2021/hash/a1a2c3fed88e9b3ba5bc3625c074a04e-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/a1a2c3fed88e9b3ba5bc3625c074a04e-Paper.pdf | REMIPS: Physically Consistent 3D Reconstruction of Multiple Interacting People under Weak Supervision | The three-dimensional reconstruction of multiple interacting humans given a monocular image is crucial for the general task of scene understanding, as capturing the subtleties of interaction is often the very reason for taking a picture. Current 3D human reconstruction methods either treat each person independently, ig... | ['Cristian Sminchisescu', 'Vlad Olaru', 'Eduard Bazavan', 'Teodor Szente', 'Mihai Zanfir', 'Mihai Fieraru'] | 2021-12-01 | null | https://openreview.net/forum?id=-AV3AKwgiG | https://openreview.net/pdf?id=-AV3AKwgiG | neurips-2021-12 | ['3d-human-reconstruction'] | ['computer-vision'] | [ 3.16356048e-02 1.33307293e-01 5.41828811e-01 -4.65944231e-01
-2.77235568e-01 -2.81245679e-01 8.59776020e-01 5.08135296e-02
-5.86764097e-01 7.33302951e-01 2.14105114e-01 5.38556218e-01
-2.50448704e-01 -6.27726734e-01 -1.20186055e+00 -5.54584086e-01
-1.19654633e-01 1.30159461e+00 1.78287908e-01 -2.95054287... | [6.9468488693237305, -1.1860027313232422] |
1c0621ef-7d70-445f-a804-be69fb7233b2 | a-semi-supervised-multi-task-learning | 2106.07381 | null | https://arxiv.org/abs/2106.07381v1 | https://arxiv.org/pdf/2106.07381v1.pdf | A Semi-supervised Multi-task Learning Approach to Classify Customer Contact Intents | In the area of customer support, understanding customers' intents is a crucial step. Machine learning plays a vital role in this type of intent classification. In reality, it is typical to collect confirmation from customer support representatives (CSRs) regarding the intent prediction, though it can unnecessarily incu... | ['Amir Biagi', 'Matthew C. Spencer', 'Li Dong'] | 2021-06-10 | null | https://aclanthology.org/2021.ecnlp-1.7 | https://aclanthology.org/2021.ecnlp-1.7.pdf | acl-ecnlp-2021-8 | ['semi-supervised-text-classification-1'] | ['natural-language-processing'] | [ 3.33391458e-01 -8.65317136e-02 -5.90579331e-01 -9.75366831e-01
-1.03471494e+00 -6.59650624e-01 4.53591466e-01 5.24248302e-01
-4.03174669e-01 5.42421639e-01 6.27477989e-02 -6.71160102e-01
-6.82532862e-02 -5.78163087e-01 -3.50982666e-01 -3.36513847e-01
2.00902075e-01 1.14072824e+00 -2.38557197e-02 -1.96666509... | [10.042876243591309, 6.113528728485107] |
2635152e-55b9-4023-9589-2a3bdefe0859 | censored-sampling-of-diffusion-models-using-3 | 2307.02770 | null | https://arxiv.org/abs/2307.02770v1 | https://arxiv.org/pdf/2307.02770v1.pdf | Censored Sampling of Diffusion Models Using 3 Minutes of Human Feedback | Diffusion models have recently shown remarkable success in high-quality image generation. Sometimes, however, a pre-trained diffusion model exhibits partial misalignment in the sense that the model can generate good images, but it sometimes outputs undesirable images. If so, we simply need to prevent the generation of ... | ['Ernest K. Ryu', 'Albert No', 'Jaewoong Cho', 'Keon Lee', 'Kibeom Myoung', 'Taeho Yoon'] | 2023-07-06 | null | null | null | null | ['image-generation'] | ['computer-vision'] | [ 2.43360072e-01 6.04594588e-01 -1.67676006e-02 -2.30566934e-01
-8.26486707e-01 -7.91470230e-01 7.73017645e-01 -2.92008072e-01
-1.84724078e-01 9.16562676e-01 2.03185216e-01 -2.87367672e-01
2.51398623e-01 -4.90849942e-01 -6.83129311e-01 -6.38526738e-01
2.99319327e-01 4.01050359e-01 -5.77611476e-02 -3.39956023... | [11.460121154785156, -0.22809697687625885] |
641855ed-a7ff-4cb3-bd74-89bf2728d85b | prototypical-cross-attention-networks-for | 2106.11958 | null | https://arxiv.org/abs/2106.11958v2 | https://arxiv.org/pdf/2106.11958v2.pdf | Prototypical Cross-Attention Networks for Multiple Object Tracking and Segmentation | Multiple object tracking and segmentation requires detecting, tracking, and segmenting objects belonging to a set of given classes. Most approaches only exploit the temporal dimension to address the association problem, while relying on single frame predictions for the segmentation mask itself. We propose Prototypical ... | ['Fisher Yu', 'Chi-Keung Tang', 'Yu-Wing Tai', 'Martin Danelljan', 'Xia Li', 'Lei Ke'] | 2021-06-22 | null | http://proceedings.neurips.cc/paper/2021/hash/093f65e080a295f8076b1c5722a46aa2-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/093f65e080a295f8076b1c5722a46aa2-Paper.pdf | neurips-2021-12 | ['video-instance-segmentation', 'multiple-object-track-and-segmentation', 'multi-object-tracking-and-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 2.01346025e-01 -3.07133555e-01 -6.21944010e-01 -2.02076137e-01
-7.37331390e-01 -4.87586051e-01 4.33638036e-01 -6.90951049e-02
-4.19328690e-01 3.39402497e-01 -2.09355295e-01 -4.10223641e-02
9.55482721e-02 -3.44439179e-01 -8.85677040e-01 -4.39460695e-01
-9.43770409e-02 5.47648847e-01 8.15219879e-01 3.96889359... | [9.007563591003418, -0.21997593343257904] |
d7c40fe8-e99f-4569-ba03-b57194716d96 | learning-attention-as-disentangler-for | 2303.15111 | null | https://arxiv.org/abs/2303.15111v1 | https://arxiv.org/pdf/2303.15111v1.pdf | Learning Attention as Disentangler for Compositional Zero-shot Learning | Compositional zero-shot learning (CZSL) aims at learning visual concepts (i.e., attributes and objects) from seen compositions and combining concept knowledge into unseen compositions. The key to CZSL is learning the disentanglement of the attribute-object composition. To this end, we propose to exploit cross-attention... | ['Kwan-Yee K. Wong', 'Kai Han', 'Shaozhe Hao'] | 2023-03-27 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Hao_Learning_Attention_As_Disentangler_for_Compositional_Zero-Shot_Learning_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Hao_Learning_Attention_As_Disentangler_for_Compositional_Zero-Shot_Learning_CVPR_2023_paper.pdf | cvpr-2023-1 | ['compositional-zero-shot-learning'] | ['computer-vision'] | [ 2.02739537e-01 -1.18205078e-01 -1.84843391e-01 -3.34299088e-01
-4.23166543e-01 -7.56185472e-01 7.88990200e-01 1.48957744e-01
-2.02507719e-01 3.75054330e-01 3.48239928e-01 9.32112783e-02
-1.31616876e-01 -8.08507919e-01 -7.88159430e-01 -1.04769790e+00
1.30661935e-01 4.22505409e-01 -2.45438889e-01 6.39769658... | [10.219938278198242, 2.2641961574554443] |
1df5a644-2f16-4dc8-8f0a-5f3ca0bea1b8 | entity-based-de-noising-modeling-for | null | null | https://aclanthology.org/2022.sigdial-1.40 | https://aclanthology.org/2022.sigdial-1.40.pdf | Entity-based De-noising Modeling for Controllable Dialogue Summarization | Although fine-tuning pre-trained backbones produces fluent and grammatically-correct text in various language generation tasks, factual consistency in abstractive summarization remains challenging. This challenge is especially thorny for dialogue summarization, where neural models often make inaccurate associations bet... | ['Nancy Chen', 'Zhengyuan Liu'] | null | null | null | null | sigdial-acl-2022-9 | ['abstractive-text-summarization'] | ['natural-language-processing'] | [ 4.47817892e-01 8.16040874e-01 -3.60171981e-02 -2.57101178e-01
-9.91425216e-01 -3.43288124e-01 9.37788546e-01 4.44813579e-01
-4.11172628e-01 1.63842368e+00 1.12568247e+00 -7.20134676e-02
1.67153612e-01 -6.47308350e-01 -5.19751906e-01 -1.51619196e-01
1.64401457e-01 8.00714552e-01 -1.82145238e-01 -5.56430697... | [12.27811336517334, 9.197392463684082] |
44cd8547-f569-46b0-a544-13962f958288 | on-the-futility-of-learning-complex-frame | 1702.00178 | null | http://arxiv.org/abs/1702.00178v2 | http://arxiv.org/pdf/1702.00178v2.pdf | On the Futility of Learning Complex Frame-Level Language Models for Chord Recognition | Chord recognition systems use temporal models to post-process frame-wise
chord preditions from acoustic models. Traditionally, first-order models such
as Hidden Markov Models were used for this task, with recent works suggesting
to apply Recurrent Neural Networks instead. Due to their ability to learn
longer-term depen... | ['Filip Korzeniowski', 'Gerhard Widmer'] | 2017-02-01 | null | null | null | null | ['chord-recognition'] | ['audio'] | [ 3.11042398e-01 2.40212709e-01 8.87446702e-02 -2.01202422e-01
-6.00635171e-01 -6.55463457e-01 6.37893796e-01 1.60384670e-01
-7.12864697e-01 4.43881691e-01 4.32422698e-01 -5.35973907e-01
-1.02974154e-01 -4.72033262e-01 -3.87714088e-01 -5.39945841e-01
-3.86768043e-01 2.45866179e-01 7.83697724e-01 -3.94106060... | [15.904216766357422, 5.341151714324951] |
4a574455-bdd0-4841-98d5-5be5632decdd | sign-language-translation-with-transformers | 2004.00588 | null | https://arxiv.org/abs/2004.00588v2 | https://arxiv.org/pdf/2004.00588v2.pdf | Better Sign Language Translation with STMC-Transformer | Sign Language Translation (SLT) first uses a Sign Language Recognition (SLR) system to extract sign language glosses from videos. Then, a translation system generates spoken language translations from the sign language glosses. This paper focuses on the translation system and introduces the STMC-Transformer which impro... | ['Jesse Read', 'Kayo Yin'] | 2020-04-01 | sign-language-translation-with-transformers-1 | https://aclanthology.org/2020.coling-main.525 | https://aclanthology.org/2020.coling-main.525.pdf | coling-2020-8 | ['sign-language-translation'] | ['computer-vision'] | [ 5.81779242e-01 -9.52489022e-03 -2.09363848e-01 -6.34600282e-01
-1.42711008e+00 -6.72023892e-01 8.25494349e-01 -8.91707838e-01
-5.50138831e-01 5.27230918e-01 6.73236966e-01 -3.98989558e-01
3.21243972e-01 -1.17782712e-01 -6.39413893e-01 -5.91916442e-01
4.16001976e-01 9.10632551e-01 2.50284672e-01 -2.01545119... | [9.204514503479004, -6.532102584838867] |
fc71d53b-01fc-4fb2-93aa-4b7f0a266ab7 | learning-deep-visual-object-models-from-noisy | 1702.08513 | null | http://arxiv.org/abs/1702.08513v1 | http://arxiv.org/pdf/1702.08513v1.pdf | Learning Deep Visual Object Models From Noisy Web Data: How to Make it Work | Deep networks thrive when trained on large scale data collections. This has
given ImageNet a central role in the development of deep architectures for
visual object classification. However, ImageNet was created during a specific
period in time, and as such it is prone to aging, as well as dataset bias
issues. Moving be... | ['Barbara Caputo', 'Jay Young', 'Nizar Massouh', 'Tatiana Tommasi', 'Nick Hawes', 'Francesca Babiloni'] | 2017-02-28 | learning-deep-visual-object-models-from-noisy-1 | null | null | ieee-xplore-2017-12 | ['object-categorization'] | ['computer-vision'] | [-1.11985654e-01 -1.48139428e-02 4.95508201e-02 -3.30644846e-01
-2.76803255e-01 -7.60384440e-01 7.24937797e-01 3.37265342e-01
-8.20959270e-01 4.82884705e-01 -1.57367066e-01 -1.93159655e-01
-2.12090045e-01 -6.89402342e-01 -9.05336142e-01 -3.91789794e-01
-2.02132434e-01 6.71901047e-01 4.20875788e-01 -3.08942020... | [9.632906913757324, 2.3769278526306152] |
08486e62-d03c-4423-9539-411d8671a231 | sgva-clip-semantic-guided-visual-adapting-of | 2211.16191 | null | https://arxiv.org/abs/2211.16191v2 | https://arxiv.org/pdf/2211.16191v2.pdf | SgVA-CLIP: Semantic-guided Visual Adapting of Vision-Language Models for Few-shot Image Classification | Although significant progress has been made in few-shot learning, most of existing few-shot image classification methods require supervised pre-training on a large amount of samples of base classes, which limits their generalization ability in real world application. Recently, large-scale Vision-Language Pre-trained mo... | ['Changsheng Xu', 'YaoWei Wang', 'Linhui Xiao', 'Xiaoshan Yang', 'Fang Peng'] | 2022-11-28 | null | null | null | null | ['few-shot-image-classification'] | ['computer-vision'] | [ 2.84196645e-01 -4.39824194e-01 -4.61812586e-01 -4.58256811e-01
-6.10778272e-01 -7.92406797e-02 7.63638914e-01 -5.74619286e-02
-4.56773132e-01 5.70287764e-01 2.58269757e-01 2.01840788e-01
6.23713769e-02 -9.77925599e-01 -6.37292266e-01 -6.90244198e-01
4.54728991e-01 6.44035786e-02 6.51354313e-01 -3.41373146... | [10.023812294006348, 2.571859121322632] |
9bb06567-425a-4633-90dd-831897f97fba | crisisltlsum-a-benchmark-for-local-crisis | 2210.14190 | null | https://arxiv.org/abs/2210.14190v1 | https://arxiv.org/pdf/2210.14190v1.pdf | CrisisLTLSum: A Benchmark for Local Crisis Event Timeline Extraction and Summarization | Social media has increasingly played a key role in emergency response: first responders can use public posts to better react to ongoing crisis events and deploy the necessary resources where they are most needed. Timeline extraction and abstractive summarization are critical technical tasks to leverage large numbers of... | ['Alejandro Jaimes', 'Joel Tetreault', 'Shihao Ran', 'Ke Zhang', 'Bashar Alhafni', 'Hossein Rajaby Faghihi'] | 2022-10-25 | null | null | null | null | ['abstractive-text-summarization'] | ['natural-language-processing'] | [-2.12666709e-02 1.99054480e-02 5.41763194e-02 -4.30899054e-01
-1.47698188e+00 -8.59514117e-01 8.49259853e-01 1.31639671e+00
-8.56187463e-01 7.43670344e-01 1.11609149e+00 -2.86109388e-01
1.42676802e-02 -6.73636854e-01 -1.13317356e-01 -3.18888634e-01
-5.04917800e-01 7.17361391e-01 1.17375471e-01 -6.40121877... | [8.720222473144531, 9.46078872680664] |
7a2686c6-68a5-440c-b950-f99a2ab9f96d | adaptive-and-scalable-android-malware | 1606.07150 | null | http://arxiv.org/abs/1606.07150v2 | http://arxiv.org/pdf/1606.07150v2.pdf | Adaptive and Scalable Android Malware Detection through Online Learning | It is well-known that malware constantly evolves so as to evade detection and
this causes the entire malware population to be non-stationary. Contrary to
this fact, prior works on machine learning based Android malware detection have
assumed that the distribution of the observed malware characteristics (i.e.,
features)... | ['Annamalai Narayanan', 'Liu Jinliang', 'Liu Yang', 'Lihui Chen'] | 2016-06-23 | null | null | null | null | ['android-malware-detection'] | ['miscellaneous'] | [ 7.02631176e-02 -4.71477300e-01 -6.26527727e-01 4.96953987e-02
-2.01456413e-01 -7.21737564e-01 6.45806968e-01 3.28779280e-01
-1.71795383e-01 3.88504744e-01 -6.42817795e-01 -5.88710904e-01
1.67754024e-01 -6.65479302e-01 -7.50375450e-01 -4.77286130e-01
-7.34795451e-01 2.26853579e-01 7.29721785e-01 -1.71831742... | [14.418810844421387, 9.678032875061035] |
5efcfe75-40d4-4c3c-8228-67d84530d6bc | you-need-to-read-again-multi-granularity | 2205.12886 | null | https://arxiv.org/abs/2205.12886v2 | https://arxiv.org/pdf/2205.12886v2.pdf | You Need to Read Again: Multi-granularity Perception Network for Moment Retrieval in Videos | Moment retrieval in videos is a challenging task that aims to retrieve the most relevant video moment in an untrimmed video given a sentence description. Previous methods tend to perform self-modal learning and cross-modal interaction in a coarse manner, which neglect fine-grained clues contained in video content, quer... | ['Xi Zhou', 'Qiong Liu', 'Jialin Gao', 'Xuan Wang', 'Xin Sun'] | 2022-05-25 | null | null | null | null | ['moment-retrieval'] | ['computer-vision'] | [ 2.06478447e-01 -4.06057209e-01 -2.56049305e-01 -3.66484612e-01
-1.03115165e+00 -4.42736596e-01 6.33985341e-01 1.01604737e-01
-6.04851186e-01 2.63786018e-01 6.48234367e-01 3.23270261e-02
-2.28457332e-01 -4.82505828e-01 -8.77397835e-01 -6.39918506e-01
2.99838781e-01 -1.03917599e-01 4.19948459e-01 -2.32964426... | [10.309380531311035, 0.9683672785758972] |
2299aa3e-daf7-4c2a-9c26-0e14134843b3 | multi-task-learning-with-high-order | 1903.12058 | null | https://arxiv.org/abs/1903.12058v2 | https://arxiv.org/pdf/1903.12058v2.pdf | Multi-Task Learning with High-Order Statistics for X-vector based Text-Independent Speaker Verification | The x-vector based deep neural network (DNN) embedding systems have demonstrated effectiveness for text-independent speaker verification. This paper presents a multi-task learning architecture for training the speaker embedding DNN with the primary task of classifying the target speakers, and the auxiliary task of reco... | ['Jun Du', 'LiRong Dai', 'Wu Guo', 'Lanhua You'] | 2019-03-28 | null | null | null | null | ['text-independent-speaker-verification'] | ['speech'] | [-2.70346999e-02 -1.25750586e-01 -2.29697358e-02 -8.21923435e-01
-8.67334604e-01 -2.05476359e-01 7.09768951e-01 -2.61181146e-01
-5.66892266e-01 2.51494437e-01 6.99161351e-01 -5.48711658e-01
2.44189650e-02 9.26550478e-03 -2.08975762e-01 -1.08488381e+00
2.73905806e-02 1.67903543e-01 -4.81179982e-01 1.11790158... | [14.3565673828125, 6.09227991104126] |
c7b8b28a-dd5c-4486-aa03-10eba41b76f9 | distributional-reinforcement-learning-with-6 | 2305.16877 | null | https://arxiv.org/abs/2305.16877v1 | https://arxiv.org/pdf/2305.16877v1.pdf | Distributional Reinforcement Learning with Dual Expectile-Quantile Regression | Successful applications of distributional reinforcement learning with quantile regression prompt a natural question: can we use other statistics to represent the distribution of returns? In particular, expectile regression is known to be more efficient than quantile regression for approximating distributions, especiall... | ['Maarten de Rijke', 'Paul Groth', 'Jean-Michel Renders', 'Romain Deffayet', 'Sami Jullien'] | 2023-05-26 | null | null | null | null | ['distributional-reinforcement-learning', 'continuous-control'] | ['methodology', 'playing-games'] | [-4.74739134e-01 -6.22484833e-02 -5.72539091e-01 -4.58786935e-01
-1.15233040e+00 -5.16741037e-01 6.72464669e-01 5.70096195e-01
-4.84475523e-01 1.17658865e+00 4.07837749e-01 -6.11650705e-01
-2.72215128e-01 -9.63546872e-01 -6.97664499e-01 -5.40000558e-01
-1.74203321e-01 5.85885704e-01 -1.06869705e-01 -3.10726672... | [4.099339962005615, 2.5989716053009033] |
9bf73746-3b6a-46f5-a55e-2caa14d0bf95 | style-normalization-and-restitution-for | 2005.11037 | null | https://arxiv.org/abs/2005.11037v1 | https://arxiv.org/pdf/2005.11037v1.pdf | Style Normalization and Restitution for Generalizable Person Re-identification | Existing fully-supervised person re-identification (ReID) methods usually suffer from poor generalization capability caused by domain gaps. The key to solving this problem lies in filtering out identity-irrelevant interference and learning domain-invariant person representations. In this paper, we aim to design a gener... | ['Wen-Jun Zeng', 'Xin Jin', 'Li Zhang', 'Zhibo Chen', 'Cuiling Lan'] | 2020-05-22 | style-normalization-and-restitution-for-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Jin_Style_Normalization_and_Restitution_for_Generalizable_Person_Re-Identification_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Jin_Style_Normalization_and_Restitution_for_Generalizable_Person_Re-Identification_CVPR_2020_paper.pdf | cvpr-2020-6 | ['generalizable-person-re-identification'] | ['computer-vision'] | [ 2.11301401e-01 -5.00037849e-01 6.24491647e-02 -6.43145382e-01
-2.31555298e-01 -6.78619742e-01 7.02705979e-01 -1.20237462e-01
-6.92866027e-01 7.36771107e-01 3.51720959e-01 1.97139516e-01
-2.59943217e-01 -6.94325924e-01 -4.48349208e-01 -8.43701720e-01
1.86874524e-01 3.32548112e-01 -1.40781283e-01 -2.86666393... | [14.737116813659668, 1.0265278816223145] |
2d8175a6-b17e-42e9-8c5b-0fe848fd3a62 | eventpoint-self-supervised-local-descriptor | 2109.00210 | null | https://arxiv.org/abs/2109.00210v3 | https://arxiv.org/pdf/2109.00210v3.pdf | EventPoint: Self-Supervised Interest Point Detection and Description for Event-based Camera | This paper proposes a self-supervised learned local detector and descriptor, called EventPoint, for event stream/camera tracking and registration. Event-based cameras have grown in popularity because of their biological inspiration and low power consumption. Despite this, applying local features directly to the event s... | ['Songzhi Su', 'Song Li', 'Cheng Zhao', 'Li Sun', 'Ze Huang'] | 2021-09-01 | null | null | null | null | ['interest-point-detection'] | ['computer-vision'] | [ 1.03696361e-01 -7.33650923e-01 -2.82171398e-01 -3.76078397e-01
-9.54552650e-01 -3.69863451e-01 6.74185991e-01 3.04847986e-01
-6.39982998e-01 3.27200502e-01 -1.09349675e-01 5.51221907e-01
-4.16164333e-03 -6.84971631e-01 -8.67691755e-01 -7.15792954e-01
-2.90560931e-01 -2.18607802e-02 6.35103643e-01 2.04294488... | [8.40736198425293, -1.2040108442306519] |
67ed08c4-ea57-4b4f-9744-29fef3ed5417 | learning-to-reason-for-text-generation-from | 2104.08296 | null | https://arxiv.org/abs/2104.08296v1 | https://arxiv.org/pdf/2104.08296v1.pdf | Learning to Reason for Text Generation from Scientific Tables | In this paper, we introduce SciGen, a new challenge dataset for the task of reasoning-aware data-to-text generation consisting of tables from scientific articles and their corresponding descriptions. Describing scientific tables goes beyond the surface realization of the table content and requires reasoning over table ... | ['Iryna Gurevych', 'Dan Roth', 'Andreas Rücklé', 'Nafise Sadat Moosavi'] | 2021-04-16 | null | https://openreview.net/forum?id=_QCy6HJJ4wd | https://openreview.net/pdf?id=_QCy6HJJ4wd | null | ['data-to-text-generation', 'arithmetic-reasoning'] | ['natural-language-processing', 'reasoning'] | [-3.50803882e-02 4.45932925e-01 3.72595601e-02 -2.09087536e-01
-1.02318394e+00 -1.00859284e+00 9.16712821e-01 6.81515872e-01
3.85263152e-02 9.41157401e-01 4.64668691e-01 -4.80079323e-01
2.55953278e-02 -1.16852570e+00 -9.16075468e-01 9.08200964e-02
3.74497473e-01 8.88043821e-01 4.76298388e-03 -4.96812463... | [11.402009010314941, 8.76620101928711] |
21b7eaf5-739d-4d12-b479-a3325b59837c | distributional-reinforcement-learning-with-5 | null | null | https://openreview.net/forum?id=C8Ltz08PtBp | https://openreview.net/pdf?id=C8Ltz08PtBp | Distributional Reinforcement Learning with Monotonic Splines | Distributional Reinforcement Learning (RL) differs from traditional RL by estimating the distribution over returns to capture the intrinsic uncertainty of MDPs. One key challenge in distributional RL lies in how to parameterize the quantile function when minimizing the Wasserstein metric of temporal differences. Existi... | ['Pascal Poupart', 'Oliver Schulte', 'Haonan Duan', 'Guiliang Liu', 'Yudong Luo'] | 2021-09-29 | null | null | null | iclr-2022-4 | ['distributional-reinforcement-learning'] | ['methodology'] | [-6.48896039e-01 3.32509726e-02 -5.00760913e-01 -5.37882924e-01
-1.57789695e+00 -6.31430805e-01 2.87718147e-01 2.43163690e-01
-7.62641668e-01 1.32600260e+00 3.22559893e-01 -1.98704392e-01
-4.48360860e-01 -7.96478748e-01 -8.92267883e-01 -7.28448272e-01
-6.07844889e-01 6.61746681e-01 -1.23946264e-01 -1.04748242... | [4.087531566619873, 2.607748508453369] |
a90fd1e7-abf8-436f-83d1-b020650880fc | interaction-relational-network-for-mutual | 1910.04963 | null | https://arxiv.org/abs/1910.04963v2 | https://arxiv.org/pdf/1910.04963v2.pdf | Interaction Relational Network for Mutual Action Recognition | Person-person mutual action recognition (also referred to as interaction recognition) is an important research branch of human activity analysis. Current solutions in the field -- mainly dominated by CNNs, GCNs and LSTMs -- often consist of complicated architectures and mechanisms to embed the relationships between the... | ['Alex C. Kot', 'Mauricio Perez', 'Jun Liu'] | 2019-10-11 | null | null | null | null | ['human-interaction-recognition'] | ['computer-vision'] | [ 6.95608407e-02 2.36663088e-01 -2.26703450e-01 -3.69472176e-01
-2.04039410e-01 -1.14881054e-01 6.93365812e-01 -3.50222647e-01
-2.57911444e-01 4.67894822e-01 5.04377902e-01 2.37473413e-01
-3.21931213e-01 -8.83430183e-01 -7.23193109e-01 -6.15022123e-01
-2.19181359e-01 7.60870814e-01 6.03279583e-02 -6.44307256... | [7.961227893829346, 0.41291823983192444] |
455d8eea-73a9-4433-946c-4eab0a3500d1 | nowj-at-coliee-2023-multi-task-and-ensemble | 2306.04903 | null | https://arxiv.org/abs/2306.04903v1 | https://arxiv.org/pdf/2306.04903v1.pdf | NOWJ at COLIEE 2023 -- Multi-Task and Ensemble Approaches in Legal Information Processing | This paper presents the NOWJ team's approach to the COLIEE 2023 Competition, which focuses on advancing legal information processing techniques and applying them to real-world legal scenarios. Our team tackles the four tasks in the competition, which involve legal case retrieval, legal case entailment, statute law retr... | ['Ha-Thanh Nguyen', 'Thai-Binh Nguyen', 'Hoang-Trung Nguyen', 'Tan-Minh Nguyen', 'Hai-Long Nguyen', 'Thi-Hai-Yen Vuong'] | 2023-06-08 | null | null | null | null | ['multi-task-learning', 'natural-language-inference'] | ['methodology', 'natural-language-processing'] | [-8.63826647e-02 -4.04289253e-02 -8.45545888e-01 -3.07439655e-01
-1.73623049e+00 -7.28406727e-01 7.33615756e-01 2.26063102e-01
-7.90117085e-01 9.10402596e-01 8.57268333e-01 -1.06072998e+00
-8.40142250e-01 -4.52772260e-01 -3.77532303e-01 2.55678713e-01
2.88406690e-03 8.78024936e-01 1.42302647e-01 -4.95170385... | [9.916411399841309, 9.220111846923828] |
48339829-3251-41dd-8575-0aa2578b3b62 | learning-document-embeddings-with-cnns | null | null | https://openreview.net/forum?id=ryHM_fbA- | https://openreview.net/pdf?id=ryHM_fbA- | Learning Document Embeddings With CNNs | This paper proposes a new model for document embedding. Existing approaches either require complex inference or use recurrent neural networks that are difficult to parallelize. We take a different route and use recent advances in language modeling to develop a convolutional neural network embedding model. This allows u... | ['Maksims Volkovs', 'Shunan Zhao', 'Chundi Lui'] | 2018-01-01 | null | null | null | iclr-2018-1 | ['document-embedding'] | ['methodology'] | [-1.46677494e-01 -2.88073737e-02 -4.76483047e-01 -4.69657958e-01
-4.29201871e-01 -5.64404607e-01 7.34624326e-01 1.53458729e-01
-5.80514014e-01 4.41484421e-01 4.40012664e-01 -6.34417117e-01
3.20650846e-01 -8.76317680e-01 -5.54222286e-01 -1.17755435e-01
-6.32263273e-02 1.53788850e-01 2.31955022e-01 -3.12391907... | [10.73326587677002, 8.17473030090332] |
dc232366-10a6-4063-b424-39dcb3f4219e | simulating-counterfactuals | 2306.15328 | null | https://arxiv.org/abs/2306.15328v1 | https://arxiv.org/pdf/2306.15328v1.pdf | Simulating counterfactuals | Counterfactual inference considers a hypothetical intervention in a parallel world that shares some evidence with the factual world. If the evidence specifies a conditional distribution on a manifold, counterfactuals may be analytically intractable. We present an algorithm for simulating values from a counterfactual di... | ['Matti Vihola', 'Santtu Tikka', 'Juha Karvanen'] | 2023-06-27 | null | null | null | null | ['fairness', 'fairness', 'counterfactual-inference'] | ['computer-vision', 'miscellaneous', 'miscellaneous'] | [-5.21032922e-02 3.38108689e-01 -3.93105447e-01 -2.74576306e-01
-4.78787839e-01 -4.29208010e-01 9.26813543e-01 -1.89177692e-01
-6.72355831e-01 1.59927213e+00 1.48385167e-01 -7.62954950e-01
-3.01645160e-01 -1.03998160e+00 -8.08775246e-01 -5.77617228e-01
-4.38658565e-01 7.39293694e-01 -4.19472277e-01 1.28218397... | [8.326698303222656, 5.511471748352051] |
87ca8ab7-abc2-47ab-b517-c9768f972eb2 | a-segmental-framework-for-fully-unsupervised | 1606.06950 | null | http://arxiv.org/abs/1606.06950v2 | http://arxiv.org/pdf/1606.06950v2.pdf | A segmental framework for fully-unsupervised large-vocabulary speech recognition | Zero-resource speech technology is a growing research area that aims to
develop methods for speech processing in the absence of transcriptions,
lexicons, or language modelling text. Early term discovery systems focused on
identifying isolated recurring patterns in a corpus, while more recent
full-coverage systems attem... | ['Sharon Goldwater', 'Aren Jansen', 'Herman Kamper'] | 2016-06-22 | null | null | null | null | ['unsupervised-speech-recognition'] | ['speech'] | [ 2.96477079e-01 2.62359619e-01 -2.41289705e-01 -5.85999668e-01
-1.39363408e+00 -5.07096052e-01 5.56846797e-01 1.19939856e-01
-5.86846113e-01 2.93798447e-01 4.03192967e-01 -3.61972570e-01
1.66856796e-01 -3.18139315e-01 -3.87219191e-01 -6.86034203e-01
-1.64519459e-01 6.73060238e-01 1.54278234e-01 -2.21672490... | [14.472715377807617, 6.653144836425781] |
1aae76bb-2554-4102-a6f4-78967c55a0a9 | dnn-filter-for-bias-reduction-in-distribution | 2211.04047 | null | https://arxiv.org/abs/2211.04047v3 | https://arxiv.org/pdf/2211.04047v3.pdf | DNN Filter for Bias Reduction in Distribution-to-Distribution Scan Matching | Distribution-to-distribution (D2D) point cloud registration techniques such as the Normal Distributions Transform (NDT) can align point clouds sampled from unstructured scenes and provide accurate bounds of their own solution error covariance -- an important feature for safety-of-life navigation tasks. D2D methods rely... | ['Jason Rife', 'Matthew McDermott'] | 2022-11-08 | null | null | null | null | ['point-cloud-registration'] | ['computer-vision'] | [ 3.22092801e-01 -1.77827835e-01 7.85476491e-02 -4.32332039e-01
-6.41705692e-01 -5.66827416e-01 6.05072379e-01 2.01462418e-01
-3.12756836e-01 6.79363132e-01 -1.27600044e-01 -5.76432832e-02
-2.66870856e-01 -1.09577453e+00 -7.75715590e-01 -5.21586120e-01
-8.83691981e-02 1.06618309e+00 4.95558113e-01 4.70427833... | [7.821380138397217, -2.8385369777679443] |
6c14d1c7-8941-42d9-a9b4-9eaeb30d3e90 | generating-text-through-adversarial-training | 1808.08703 | null | https://arxiv.org/abs/1808.08703v3 | https://arxiv.org/pdf/1808.08703v3.pdf | Generating Text through Adversarial Training using Skip-Thought Vectors | GANs have been shown to perform exceedingly well on tasks pertaining to image generation and style transfer. In the field of language modelling, word embeddings such as GLoVe and word2vec are state-of-the-art methods for applying neural network models on textual data. Attempts have been made to utilize GANs with word e... | ['Afroz Ahamad'] | 2018-08-27 | generating-text-through-adversarial-training-1 | https://aclanthology.org/N19-3008 | https://aclanthology.org/N19-3008.pdf | naacl-2019-6 | ['conditional-text-generation'] | ['natural-language-processing'] | [ 4.10924405e-01 3.47049683e-01 4.21121903e-02 -2.81972855e-01
-7.27761209e-01 -3.40112716e-01 1.32616675e+00 -4.43281800e-01
-3.61073315e-01 1.16264582e+00 6.29303038e-01 -1.70478076e-01
5.84150553e-01 -8.30380321e-01 -4.59758669e-01 -3.93361181e-01
6.10630512e-01 3.77236217e-01 -6.77396297e-01 -4.19023067... | [11.879911422729492, 9.352286338806152] |
4be019e0-a213-4668-96f3-6a13bf4d6215 | lnl-k-learning-with-noisy-labels-and-noise | 2306.11911 | null | https://arxiv.org/abs/2306.11911v1 | https://arxiv.org/pdf/2306.11911v1.pdf | LNL+K: Learning with Noisy Labels and Noise Source Distribution Knowledge | Learning with noisy labels (LNL) is challenging as the model tends to memorize noisy labels, which can lead to overfitting. Many LNL methods detect clean samples by maximizing the similarity between samples in each category, which does not make any assumptions about likely noise sources. However, we often have some kno... | ['Bryan A. Plummer', 'Siqi Wang'] | 2023-06-20 | null | null | null | null | ['learning-with-noisy-labels', 'learning-with-noisy-labels'] | ['computer-vision', 'natural-language-processing'] | [ 2.60596901e-01 -6.12706989e-02 -5.15772635e-03 -3.26190859e-01
-1.18398356e+00 -8.92895699e-01 5.59476912e-01 2.71656573e-01
-4.20769811e-01 6.50533974e-01 9.93027166e-02 -7.68825784e-02
6.51924983e-02 -6.52625203e-01 -9.34568405e-01 -9.96270061e-01
2.32958764e-01 1.11741200e-01 5.07094339e-03 2.08948985... | [9.400724411010742, 3.927699327468872] |
b2aed967-48cd-451e-b292-6788b2137309 | a-hierarchical-spatio-temporal-graph | 2112.04294 | null | https://arxiv.org/abs/2112.04294v2 | https://arxiv.org/pdf/2112.04294v2.pdf | A Hierarchical Spatio-Temporal Graph Convolutional Neural Network for Anomaly Detection in Videos | Deep learning models have been widely used for anomaly detection in surveillance videos. Typical models are equipped with the capability to reconstruct normal videos and evaluate the reconstruction errors on anomalous videos to indicate the extent of abnormalities. However, existing approaches suffer from two disadvant... | ['Zifeng Qiu', 'Yafeng Hao', 'Hongguang Li', 'Wenrui Ding', 'Yalong Jiang', 'Xianlin Zeng'] | 2021-12-08 | null | null | null | null | ['anomaly-detection-in-surveillance-videos', 'anomaly-detection-in-surveillance-videos'] | ['computer-vision', 'methodology'] | [-1.62315011e-01 -1.04517214e-01 9.75891724e-02 -4.91121002e-02
2.74387777e-01 -1.32542118e-01 5.08098125e-01 4.28611748e-02
-1.54764250e-01 1.70937553e-01 4.09236252e-01 -1.19150672e-02
8.85033831e-02 -8.30267429e-01 -7.58784890e-01 -6.11478448e-01
-5.33168316e-01 1.39506295e-01 6.31288052e-01 -3.79665524... | [7.859288215637207, 1.5446853637695312] |
715ece65-0744-499c-a4e5-bff8b1262c5c | learning-formation-energy-of-inorganic | 1901.06016 | null | http://arxiv.org/abs/1901.06016v2 | http://arxiv.org/pdf/1901.06016v2.pdf | Learning formation energy of inorganic compounds using matrix variate deep Gaussian process | Future advancement of engineering applications is dependent on design of
novel materials with desired properties. Enormous size of known chemical space
necessitates use of automated high throughput screening to search the desired
material. The high throughput screening uses quantum chemistry calculations to
predict mat... | ['Saket Mishra', 'Piyush Tagade'] | 2018-12-22 | null | null | null | null | ['formation-energy'] | ['miscellaneous'] | [ 2.37902135e-01 -3.68088573e-01 2.17452630e-01 -1.55419111e-01
-6.87172592e-01 -4.41716164e-01 5.10831833e-01 5.27015686e-01
-4.50356632e-01 1.23294449e+00 -4.32388633e-01 -3.73866796e-01
-7.72382393e-02 -1.24738514e+00 -7.59842575e-01 -9.41612840e-01
1.90984622e-01 4.61630851e-01 2.27534413e-01 -1.76739037... | [5.190478324890137, 5.394010543823242] |
acf087d4-c030-40b7-ab33-bab7a3bebad8 | lea-improving-sentence-similarity-robustness | 2307.02912 | null | https://arxiv.org/abs/2307.02912v1 | https://arxiv.org/pdf/2307.02912v1.pdf | LEA: Improving Sentence Similarity Robustness to Typos Using Lexical Attention Bias | Textual noise, such as typos or abbreviations, is a well-known issue that penalizes vanilla Transformers for most downstream tasks. We show that this is also the case for sentence similarity, a fundamental task in multiple domains, e.g. matching, retrieval or paraphrasing. Sentence similarity can be approached using cr... | ['David Jiménez', 'Diego Ortego', 'Emilio Almazán', 'Mario Almagro'] | 2023-07-06 | null | null | null | null | ['natural-language-inference'] | ['natural-language-processing'] | [ 4.21407133e-01 -2.41219342e-01 -7.07494169e-02 -3.20655704e-01
-9.37213123e-01 -7.28785336e-01 5.72824359e-01 5.10541081e-01
-7.20517874e-01 4.39179361e-01 4.77443993e-01 -4.45196599e-01
-1.95122540e-01 -8.20482552e-01 -1.00029385e+00 -3.47060770e-01
4.78979468e-01 3.54072779e-01 -8.69494956e-03 -5.21030426... | [11.034833908081055, 8.927603721618652] |
49f837b3-f773-40c9-bfa7-c774294fc254 | mdg-metaphorical-sentence-detection-and | null | null | https://openreview.net/forum?id=mPLmX6RVDT2 | https://openreview.net/pdf?id=mPLmX6RVDT2 | MDG: Metaphorical Sentence Detection and Generation with Masked Metaphor Modeling | This study tackles literal to metaphorical sentence generation, presenting a framework that can potentially lead to the production of an infinite number of new metaphors. To achieve this goal, we propose a complete workflow that tackles metaphorical sentence classification and metaphor reconstruction. Unlike similar re... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['sentence-classification'] | ['natural-language-processing'] | [ 5.61583191e-02 4.70146000e-01 4.15524900e-01 -2.34162241e-01
-2.35843077e-01 -7.73211181e-01 1.41204786e+00 4.58496690e-01
-2.51786560e-01 8.70460093e-01 5.95614851e-01 -3.15499723e-01
1.04681879e-01 -1.05216980e+00 -2.67619103e-01 -3.56008351e-01
2.42324919e-01 7.98866510e-01 -2.43000627e-01 -9.65695500... | [11.118101119995117, 9.13170337677002] |
7cde111b-eaee-4fc5-959a-ffd3572a4d99 | towards-semi-supervised-deep-facial | 2203.12341 | null | https://arxiv.org/abs/2203.12341v2 | https://arxiv.org/pdf/2203.12341v2.pdf | Towards Semi-Supervised Deep Facial Expression Recognition with An Adaptive Confidence Margin | Only parts of unlabeled data are selected to train models for most semi-supervised learning methods, whose confidence scores are usually higher than the pre-defined threshold (i.e., the confidence margin). We argue that the recognition performance should be further improved by making full use of all unlabeled data. In ... | ['Xinbo Gao', 'Xiaoyu Wang', 'Xi Yang', 'Nannan Wang', 'Hangyu Li'] | 2022-03-23 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Li_Towards_Semi-Supervised_Deep_Facial_Expression_Recognition_With_an_Adaptive_Confidence_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Li_Towards_Semi-Supervised_Deep_Facial_Expression_Recognition_With_an_Adaptive_Confidence_CVPR_2022_paper.pdf | cvpr-2022-1 | ['facial-expression-recognition'] | ['computer-vision'] | [ 2.06451297e-01 1.93620682e-01 -7.26352394e-01 -9.08987582e-01
-9.92386520e-01 -3.58597696e-01 2.38784060e-01 -1.13120459e-01
-4.81589913e-01 7.30450273e-01 -7.92930499e-02 2.37677902e-01
3.58351856e-01 -4.13327068e-01 -4.59416896e-01 -9.43525434e-01
1.33177340e-01 2.55276978e-01 -3.01588565e-01 6.12357594... | [13.564806938171387, 1.637524127960205] |
5ab9fefd-9041-4d19-9677-4acaade4b398 | advances-and-challenges-in-meta-learning-a | 2307.04722 | null | https://arxiv.org/abs/2307.04722v1 | https://arxiv.org/pdf/2307.04722v1.pdf | Advances and Challenges in Meta-Learning: A Technical Review | Meta-learning empowers learning systems with the ability to acquire knowledge from multiple tasks, enabling faster adaptation and generalization to new tasks. This review provides a comprehensive technical overview of meta-learning, emphasizing its importance in real-world applications where data may be scarce or expen... | ['KC Santosh', 'Thorsteinn Rögnvaldsson', 'Joaquin Vanschoren', 'Mohamed-Rafik Bouguelia', 'Anna Vettoruzzo'] | 2023-07-10 | null | null | null | null | ['self-supervised-learning', 'meta-learning', 'domain-adaptation', 'continual-learning', 'federated-learning', 'personalized-federated-learning', 'multi-task-learning', 'transfer-learning'] | ['computer-vision', 'methodology', 'methodology', 'methodology', 'methodology', 'methodology', 'methodology', 'miscellaneous'] | [ 1.29535735e-01 -9.66302231e-02 -4.56866592e-01 -2.60404348e-01
-9.58073854e-01 -3.52510065e-01 3.65287811e-01 3.54799628e-01
-3.61081272e-01 9.13866103e-01 -1.75606892e-01 -1.51944542e-02
-4.92856234e-01 -6.21538758e-01 -5.90653598e-01 -8.42223108e-01
-3.34797710e-01 4.80589539e-01 -2.57668287e-01 -2.82207102... | [9.922472953796387, 3.163855791091919] |
2083c277-cc5f-488b-bd50-3b1382a35bb2 | genetic-algorithm-for-program-synthesis | 2211.11937 | null | https://arxiv.org/abs/2211.11937v2 | https://arxiv.org/pdf/2211.11937v2.pdf | Genetic Algorithm for Program Synthesis | A deductive program synthesis tool takes a specification as input and derives a program that satisfies the specification. The drawback of this approach is that search spaces for such correct programs tend to be enormous, making it difficult to derive correct programs within a realistic timeout. To speed up such program... | ['Yutaka Nagashima'] | 2022-11-22 | null | null | null | null | ['program-synthesis'] | ['computer-code'] | [ 4.66424674e-01 3.40304136e-01 -8.79513994e-02 -3.18216205e-01
-2.82750100e-01 -5.78229129e-01 4.32661086e-01 5.72838113e-02
8.74085817e-03 9.15927708e-01 -3.77866060e-01 -7.86418557e-01
-5.66043817e-02 -1.22556627e+00 -5.45808434e-01 -1.44796118e-01
2.48379171e-01 2.36707509e-01 5.72312951e-01 -3.82271469... | [8.068137168884277, 7.3107590675354] |
826c7f9a-3b26-48fa-9e88-ff3118033fd2 | compound-figure-separation-of-biomedical | 2107.08650 | null | https://arxiv.org/abs/2107.08650v1 | https://arxiv.org/pdf/2107.08650v1.pdf | Compound Figure Separation of Biomedical Images with Side Loss | Unsupervised learning algorithms (e.g., self-supervised learning, auto-encoder, contrastive learning) allow deep learning models to learn effective image representations from large-scale unlabeled data. In medical image analysis, even unannotated data can be difficult to obtain for individual labs. Fortunately, nationa... | ['Yuankai Huo', 'Haichun Yang', 'Catie Chang', 'Bennett A. Landman', 'Agnes B. Fogo', 'Mengyang Zhao', 'Shunxing Bao', 'Aadarsh Jha', 'Jiachen Xu', 'Yuanhan Tian', 'Ruining Deng', 'Quan Liu', 'Chang Qu', 'Tianyuan Yao'] | 2021-07-19 | null | null | null | null | ['image-augmentation'] | ['computer-vision'] | [ 2.36307174e-01 1.26888767e-01 -3.46578002e-01 -4.00064975e-01
-1.12915361e+00 -5.29481947e-01 2.08991319e-01 3.46468627e-01
-3.61349314e-01 6.73677683e-01 -2.77912080e-01 -4.69017535e-01
3.12263995e-01 -6.13574862e-01 -8.89018118e-01 -5.77120125e-01
7.40318969e-02 1.83455050e-01 1.11644395e-01 5.49925528... | [15.041478157043457, -2.5126662254333496] |
c8fcfbaf-ba0e-400c-bf9c-53aaa6d99797 | applying-deep-reinforcement-learning-to-the | 2211.14939 | null | https://arxiv.org/abs/2211.14939v2 | https://arxiv.org/pdf/2211.14939v2.pdf | Applying Deep Reinforcement Learning to the HP Model for Protein Structure Prediction | A central problem in computational biophysics is protein structure prediction, i.e., finding the optimal folding of a given amino acid sequence. This problem has been studied in a classical abstract model, the HP model, where the protein is modeled as a sequence of H (hydrophobic) and P (polar) amino acids on a lattice... | ['Liang Dai', 'Roland H. C. Yap', 'Fujia Tian', 'Adithya Murali', 'Olafs Vandans', 'Houjing Huang', 'Kaiyuan Yang'] | 2022-11-27 | null | null | null | null | ['protein-folding'] | ['natural-language-processing'] | [ 6.99398145e-02 9.17660743e-02 -1.23740308e-01 -1.63430229e-01
-7.95128345e-01 -5.31420767e-01 -1.31691590e-01 1.21907674e-01
-6.67901695e-01 1.29534507e+00 -1.87833294e-01 -7.51675010e-01
1.74488306e-01 -6.71200514e-01 -1.46927679e+00 -1.09570456e+00
-4.65094686e-01 7.41860509e-01 -7.05113038e-02 -2.69693613... | [4.7280402183532715, 5.582693576812744] |
11d6832a-36b9-40d1-ab9a-c0bc6fcc26f7 | pay-attention-to-relations-multi-embeddings | 2203.01903 | null | https://arxiv.org/abs/2203.01903v1 | https://arxiv.org/pdf/2203.01903v1.pdf | Pay Attention to Relations: Multi-embeddings for Attributed Multiplex Networks | Graph Convolutional Neural Networks (GCNs) have become effective machine learning algorithms for many downstream network mining tasks such as node classification, link prediction, and community detection. However, most GCN methods have been developed for homogenous networks and are limited to a single embedding for eac... | ['Siddharth Krishnan', 'Michael Ridenhour', 'Joshua Melton'] | 2022-03-03 | null | null | null | null | ['network-embedding'] | ['methodology'] | [ 5.52265085e-02 3.59420121e-01 -7.87762225e-01 -6.55272603e-02
9.76551771e-02 -7.30084896e-01 9.58500803e-01 5.82682908e-01
-1.65774748e-02 4.86881196e-01 5.51121473e-01 -4.01877731e-01
-7.06847429e-01 -1.32545161e+00 -5.02422035e-01 -5.22071183e-01
-7.54055142e-01 7.88477838e-01 2.15859473e-01 -3.85397553... | [7.076934337615967, 6.1961798667907715] |
27b608c7-d6db-40b1-bae2-95e1638a5044 | raven-s-progressive-matrices-completion-with | 2103.12045 | null | https://arxiv.org/abs/2103.12045v2 | https://arxiv.org/pdf/2103.12045v2.pdf | Raven's Progressive Matrices Completion with Latent Gaussian Process Priors | Abstract reasoning ability is fundamental to human intelligence. It enables humans to uncover relations among abstract concepts and further deduce implicit rules from the relations. As a well-known abstract visual reasoning task, Raven's Progressive Matrices (RPM) are widely used in human IQ tests. Although extensive r... | ['xiangyang xue', 'Bin Li', 'Fan Shi'] | 2021-03-22 | null | null | null | null | ['answer-selection'] | ['natural-language-processing'] | [ 2.43543819e-01 2.23180488e-01 1.03557728e-01 -3.45106155e-01
-4.55754846e-02 -4.05694932e-01 6.86808109e-01 -3.43641527e-02
-4.16833311e-02 7.16721654e-01 -7.59736896e-02 -3.94065738e-01
-5.37390590e-01 -9.74633217e-01 -5.53080916e-01 -5.47273993e-01
2.61321276e-01 1.08837306e+00 6.47630543e-02 -6.62591830... | [10.625925064086914, 2.004633665084839] |
aaa00da2-9aaa-41c4-b6df-0a4dcd19e9ee | deep-multi-view-subspace-clustering-with | 2305.06939 | null | https://arxiv.org/abs/2305.06939v1 | https://arxiv.org/pdf/2305.06939v1.pdf | Deep Multi-View Subspace Clustering with Anchor Graph | Deep multi-view subspace clustering (DMVSC) has recently attracted increasing attention due to its promising performance. However, existing DMVSC methods still have two issues: (1) they mainly focus on using autoencoders to nonlinearly embed the data, while the embedding may be suboptimal for clustering because the clu... | ['Lifang He', 'Xiaorong Pu', 'Jingyu Pu', 'Yazhou Ren', 'Chenhang Cui'] | 2023-05-11 | null | null | null | null | ['multi-view-subspace-clustering'] | ['computer-vision'] | [-3.41180950e-01 -4.28473264e-01 6.74980953e-02 -1.98797598e-01
-4.97023493e-01 -5.07112503e-01 3.93541157e-01 -1.74114943e-01
-1.75379783e-01 7.64182806e-02 3.53325337e-01 2.15036750e-01
-2.51812458e-01 -5.29982984e-01 -3.94024104e-01 -1.13684869e+00
1.29578978e-01 2.56173790e-01 2.95666791e-02 1.67502448... | [8.397245407104492, 4.183085918426514] |
7aaaf7e2-db4c-4175-aea0-bb9c21cf8a6a | verify-and-edit-a-knowledge-enhanced-chain-of | 2305.03268 | null | https://arxiv.org/abs/2305.03268v1 | https://arxiv.org/pdf/2305.03268v1.pdf | Verify-and-Edit: A Knowledge-Enhanced Chain-of-Thought Framework | As large language models (LLMs) have become the norm in NLP, demonstrating good performance in generation and reasoning tasks, one of its most fatal disadvantages is the lack of factual correctness. Generating unfactual texts not only leads to lower performances but also degrades the trust and validity of their applica... | ['Lidong Bing', 'Chengwei Qin', 'Shafiq Joty', 'Xingxuan Li', 'Ruochen Zhao'] | 2023-05-05 | null | null | null | null | ['open-domain-question-answering'] | ['natural-language-processing'] | [ 1.90639514e-02 8.57781589e-01 4.18285616e-02 -4.32012469e-01
-7.50551462e-01 -4.36752141e-01 7.90101051e-01 3.68145257e-01
-1.15410857e-01 9.39368248e-01 2.76314795e-01 -6.91295326e-01
-1.73810542e-01 -9.14840817e-01 -6.95704877e-01 2.41706565e-01
6.12199724e-01 7.30373681e-01 1.86376452e-01 -4.31463867... | [10.075693130493164, 7.676489353179932] |
90ab6010-5a3b-4a5b-8ae8-ef0e63bb250f | generative-logic-with-time-beyond-logical | 2301.08509 | null | https://arxiv.org/abs/2301.08509v2 | https://arxiv.org/pdf/2301.08509v2.pdf | Generative Logic with Time: Beyond Logical Consistency and Statistical Possibility | This paper gives a simple theory of inference to logically reason symbolic knowledge fully from data over time. We take a Bayesian approach to model how data causes symbolic knowledge. Probabilistic reasoning with symbolic knowledge is modelled as a process of going the causality forwards and backwards. The forward and... | ['Hiroyuki Kido'] | 2023-01-20 | null | null | null | null | ['formal-logic'] | ['reasoning'] | [ 3.37770313e-01 1.01087594e+00 -3.11427563e-02 -6.54755533e-01
-2.00828165e-01 -5.06938577e-01 1.07636869e+00 -3.55814360e-02
-3.06295574e-01 8.40704679e-01 9.21808705e-02 -6.80689216e-01
-6.05730534e-01 -9.81671453e-01 -9.96461689e-01 -6.29417896e-01
-4.36987072e-01 9.34387565e-01 6.19495571e-01 -5.65800183... | [8.60002613067627, 6.586549282073975] |
8439f68b-06f5-40b2-9596-91b7c0e1e07e | signet-semantic-instance-aided-unsupervised | 1812.05642 | null | http://arxiv.org/abs/1812.05642v2 | http://arxiv.org/pdf/1812.05642v2.pdf | SIGNet: Semantic Instance Aided Unsupervised 3D Geometry Perception | Unsupervised learning for geometric perception (depth, optical flow, etc.) is
of great interest to autonomous systems. Recent works on unsupervised learning
have made considerable progress on perceiving geometry; however, they usually
ignore the coherence of objects and perform poorly under scenarios with dark
and nois... | ['Aman Raj', 'Samuel Sunarjo', 'Dinesh Bharadia', 'Rui Guo', 'Yue Meng', 'Yongxi Lu', 'Tara Javidi', 'Gaurav Bansal'] | 2018-12-13 | signet-semantic-instance-aided-unsupervised-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Meng_SIGNet_Semantic_Instance_Aided_Unsupervised_3D_Geometry_Perception_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Meng_SIGNet_Semantic_Instance_Aided_Unsupervised_3D_Geometry_Perception_CVPR_2019_paper.pdf | cvpr-2019-6 | ['3d-geometry-perception'] | ['computer-vision'] | [ 2.88926885e-02 1.24645106e-01 -1.75044402e-01 -6.13624752e-01
-2.16878101e-01 -5.22160828e-01 6.00138903e-01 1.25355840e-01
-2.45395780e-01 5.59837639e-01 1.66877329e-01 7.20616207e-02
-2.57247686e-02 -9.03937876e-01 -4.57365364e-01 -7.35353112e-01
-2.18390003e-02 4.13057059e-01 4.27805007e-01 2.25882113... | [8.62179946899414, -1.975090503692627] |
f7abcafc-1be9-44e2-ae44-85152b2c8d5e | privacy-preserving-household-load-forecasting | 2206.15192 | null | https://arxiv.org/abs/2206.15192v1 | https://arxiv.org/pdf/2206.15192v1.pdf | Privacy-preserving household load forecasting based on non-intrusive load monitoring: A federated deep learning approach | Load forecasting is very essential in the analysis and grid planning of power systems. For this reason, we first propose a household load forecasting method based on federated deep learning and non-intrusive load monitoring (NILM). For all we know, this is the first research on federated learning (FL) in household load... | ['Jianhong Pan', 'Jian Wang', 'Jingru Feng', 'Xinxin Zhou'] | 2022-06-30 | null | null | null | null | ['non-intrusive-load-monitoring', 'load-forecasting', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring'] | ['knowledge-base', 'miscellaneous', 'miscellaneous', 'time-series'] | [-5.77858210e-01 -6.07370026e-02 -1.92280427e-01 -3.59512448e-01
-1.15432613e-01 -3.09586704e-01 2.66616285e-01 -3.21643353e-01
2.66924232e-01 8.07044864e-01 8.51236805e-02 -7.38602057e-02
-1.19531803e-01 -1.14864337e+00 -3.95148367e-01 -1.27243912e+00
-3.12878162e-01 1.18319847e-01 -6.68808937e-01 1.67273656... | [5.893728256225586, 2.742708683013916] |
49f55f83-96da-42ee-926c-31e8a3a1f789 | knowledge-distillation-with-error-correcting | 2204.00649 | null | https://arxiv.org/abs/2204.00649v1 | https://arxiv.org/pdf/2204.00649v1.pdf | Knowledge distillation with error-correcting transfer learning for wind power prediction | Wind power prediction, especially for turbines, is vital for the operation, controllability, and economy of electricity companies. Hybrid methodologies combining advanced data science with weather forecasting have been incrementally applied to the predictions. Nevertheless, individually modeling massive turbines from s... | ['Hao Chen'] | 2022-04-01 | null | null | null | null | ['weather-forecasting'] | ['miscellaneous'] | [-3.58441740e-01 -9.88697112e-02 2.31513567e-02 8.70386884e-02
-1.73159033e-01 -5.66387177e-01 5.11152923e-01 -8.31262916e-02
-9.72329751e-02 1.09558904e+00 -8.53524953e-02 -4.00830418e-01
-3.89251560e-01 -1.10209942e+00 -3.22876960e-01 -7.54288852e-01
-5.66555917e-01 3.30232233e-01 -1.50971964e-01 -6.95535600... | [6.311424732208252, 2.880448818206787] |
fc2ae55f-d9d0-414f-a27e-86fe7d5dc13f | garmentnets-category-level-pose-estimation | 2104.05177 | null | https://arxiv.org/abs/2104.05177v2 | https://arxiv.org/pdf/2104.05177v2.pdf | GarmentNets: Category-Level Pose Estimation for Garments via Canonical Space Shape Completion | This paper tackles the task of category-level pose estimation for garments. With a near infinite degree of freedom, a garment's full configuration (i.e., poses) is often described by the per-vertex 3D locations of its entire 3D surface. However, garments are also commonly subject to extreme cases of self-occlusion, esp... | ['Shuran Song', 'Cheng Chi'] | 2021-04-12 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Chi_GarmentNets_Category-Level_Pose_Estimation_for_Garments_via_Canonical_Space_Shape_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Chi_GarmentNets_Category-Level_Pose_Estimation_for_Garments_via_Canonical_Space_Shape_ICCV_2021_paper.pdf | iccv-2021-1 | ['3d-shape-representation'] | ['computer-vision'] | [ 1.61333784e-01 8.54954496e-02 6.06820136e-02 -2.07125813e-01
-6.20265007e-01 -8.13482702e-01 3.44185024e-01 -2.47931123e-01
2.32930481e-01 2.80986667e-01 -1.80016905e-01 1.78496048e-01
-1.83960915e-01 -7.83996224e-01 -9.78338122e-01 -6.74526334e-01
-3.59465368e-02 1.06306612e+00 -1.10906258e-01 -1.12818740... | [8.308576583862305, -3.187530994415283] |
96eb5937-0aed-44c9-9a6b-a18c3385d37c | deep-reinforcement-learning-for-cost | 2302.10261 | null | https://arxiv.org/abs/2302.10261v2 | https://arxiv.org/pdf/2302.10261v2.pdf | Deep Reinforcement Learning for Cost-Effective Medical Diagnosis | Dynamic diagnosis is desirable when medical tests are costly or time-consuming. In this work, we use reinforcement learning (RL) to find a dynamic policy that selects lab test panels sequentially based on previous observations, ensuring accurate testing at a low cost. Clinical diagnostic data are often highly imbalance... | ['Mengdi Wang', 'Yuan Luo', 'Kaixuan Huang', 'Joseph Kim', 'Yikuan Li', 'Zheng Yu'] | 2023-02-20 | null | null | null | null | ['mortality-prediction', 'medical-diagnosis'] | ['medical', 'medical'] | [-4.50560451e-02 5.62738106e-02 -2.96161085e-01 -3.09932441e-01
-9.26558733e-01 -2.78161675e-01 -2.76678771e-01 4.07347381e-01
-2.51622885e-01 9.59435046e-01 -3.16090524e-01 -6.46521926e-01
-6.49554431e-01 -6.72982216e-01 -7.10311770e-01 -6.95743084e-01
-4.00037706e-01 1.05257773e+00 -3.57131511e-01 3.74302417... | [4.030922889709473, 2.716982364654541] |
3a22876f-af18-4224-a6f0-3d1e144925e0 | self-paced-deep-regression-forests-with | 2004.01459 | null | https://arxiv.org/abs/2004.01459v4 | https://arxiv.org/pdf/2004.01459v4.pdf | Self-Paced Deep Regression Forests with Consideration on Underrepresented Examples | Deep discriminative models (e.g. deep regression forests, deep neural decision forests) have achieved remarkable success recently to solve problems such as facial age estimation and head pose estimation. Most existing methods pursue robust and unbiased solutions either through learning discriminative features, or rewei... | ['Lili Pan', 'Zenglin Xu', 'Yazhou Ren', 'Shijie Ai'] | 2020-04-03 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/7424_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123750273.pdf | eccv-2020-8 | ['head-pose-estimation'] | ['computer-vision'] | [-1.85527384e-01 1.22848161e-01 -3.20729554e-01 -9.08146679e-01
-4.15751368e-01 -3.25492099e-02 7.02424228e-01 -3.64825279e-01
-6.76971555e-01 1.01704752e+00 3.67479682e-01 7.20537901e-02
-1.11614577e-01 -6.68064833e-01 -2.53088713e-01 -1.10071504e+00
5.51536642e-02 6.20499730e-01 -2.24850535e-01 3.11083975... | [13.606466293334961, 0.9083948135375977] |
32272b25-209b-419e-a386-25b01da3ec05 | superior-performance-with-diversified | null | null | https://openreview.net/forum?id=tvwNdOKhuF5 | https://openreview.net/pdf?id=tvwNdOKhuF5 | Superior Performance with Diversified Strategic Control in FPS Games Using General Reinforcement Learning | This paper offers an overall solution for first-person shooter (FPS) games to achieve superior performance using general reinforcement learning (RL). We introduce an agent in ViZDoom that can surpass previous top agents ranked in the open ViZDoom AI Competitions by a large margin. The proposed framework consists of a n... | ['Lei Han', 'Zhengyou Zhang', 'Zhuobin Zheng', 'Peng Sun', 'Chun Yuan', 'Jiawei Xu', 'Shuxing Li'] | 2021-09-29 | null | null | null | null | ['fps-games'] | ['playing-games'] | [-3.84041518e-01 -1.05519732e-02 -6.82633668e-02 1.36767671e-01
-3.55558068e-01 -5.66165149e-01 6.66339695e-01 -3.96136850e-01
-8.31636906e-01 1.34297431e+00 2.52034158e-01 -1.76337346e-01
-9.28198755e-01 -4.09803838e-01 -4.63339150e-01 -8.28339815e-01
-3.87795299e-01 7.76075244e-01 4.72577780e-01 -1.26467061... | [3.666414976119995, 1.6032196283340454] |
90d06537-f822-4bb4-afda-6f9511790614 | a-neural-approach-to-automated-essay-scoring | null | null | https://aclanthology.org/D16-1193 | https://aclanthology.org/D16-1193.pdf | A Neural Approach to Automated Essay Scoring | null | ['Hwee Tou Ng', 'Kaveh Taghipour'] | 2016-11-01 | null | null | null | emnlp-2016-11 | ['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... | [-7.42611026763916, 3.758726119995117] |
67f42db2-4cda-460e-b1cf-96016e3b2723 | improving-dependency-parsers-with-supertags | null | null | https://aclanthology.org/E14-4030 | https://aclanthology.org/E14-4030.pdf | Improving Dependency Parsers with Supertags | null | ['Yuji Matsumoto', 'Kevin Duh', 'Hiroki Ouchi'] | 2014-04-01 | null | null | null | eacl-2014-4 | ['transition-based-dependency-parsing'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.354365348815918, 3.66866135597229] |
e7351d1e-7c15-4e48-ba33-1daea16910b2 | learning-to-generate-language-supervised-and | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhang_Learning_To_Generate_Language-Supervised_and_Open-Vocabulary_Scene_Graph_Using_Pre-Trained_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhang_Learning_To_Generate_Language-Supervised_and_Open-Vocabulary_Scene_Graph_Using_Pre-Trained_CVPR_2023_paper.pdf | Learning To Generate Language-Supervised and Open-Vocabulary Scene Graph Using Pre-Trained Visual-Semantic Space | Scene graph generation (SGG) aims to abstract an image into a graph structure, by representing objects as graph nodes and their relations as labeled edges. However, two knotty obstacles limit the practicability of current SGG methods in real-world scenarios: 1) training SGG models requires time-consuming ground-tru... | ['Chang-Wen Chen', 'Tao Mei', 'Rui Huang', 'Ting Yao', 'Yingwei Pan', 'Yong Zhang'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['open-vocabulary-object-detection', 'scene-graph-generation', 'word-alignment'] | ['computer-vision', 'computer-vision', 'natural-language-processing'] | [ 5.72647214e-01 6.32882655e-01 -2.74752468e-01 -3.33327621e-01
-5.07246494e-01 -6.49043739e-01 8.33151996e-01 2.98778594e-01
-4.61930335e-02 2.18389317e-01 -2.89908964e-02 -3.17205817e-01
1.28177688e-01 -1.02124429e+00 -9.44694340e-01 -4.64559942e-01
6.88482597e-02 3.17210525e-01 3.94574910e-01 -1.58878639... | [10.372176170349121, 1.6105009317398071] |
fddc475b-8485-45a9-9c44-16a8614523ea | pso-convolutional-neural-networks-with | 2205.10456 | null | https://arxiv.org/abs/2205.10456v2 | https://arxiv.org/pdf/2205.10456v2.pdf | PSO-Convolutional Neural Networks with Heterogeneous Learning Rate | Convolutional Neural Networks (ConvNets or CNNs) have been candidly deployed in the scope of computer vision and related fields. Nevertheless, the dynamics of training of these neural networks lie still elusive: it is hard and computationally expensive to train them. A myriad of architectures and training strategies ha... | ['Bernardete Ribeiro', 'Augusto Santos', 'Nguyen Huu Phong'] | 2022-05-20 | null | null | null | null | ['pso-convnets-dynamics-1', 'pso-convnets-dynamics-2'] | ['computer-vision', 'computer-vision'] | [ 5.74794300e-02 -1.89101592e-01 1.47495747e-01 -1.47041798e-01
-8.63175169e-02 -3.26355755e-01 6.79996133e-01 1.51288047e-01
-1.05074561e+00 6.93967223e-01 -4.94882137e-01 -1.27643257e-01
-3.58630657e-01 -7.33288407e-01 -7.56401896e-01 -1.11530435e+00
-1.77669004e-01 3.44647467e-01 4.14018244e-01 -2.70286560... | [8.426104545593262, 3.1744253635406494] |
2bc41dda-a1b3-4f23-b20a-c5ce6731f244 | dt2i-dense-text-to-image-generation-from | 2204.02035 | null | https://arxiv.org/abs/2204.02035v1 | https://arxiv.org/pdf/2204.02035v1.pdf | DT2I: Dense Text-to-Image Generation from Region Descriptions | Despite astonishing progress, generating realistic images of complex scenes remains a challenging problem. Recently, layout-to-image synthesis approaches have attracted much interest by conditioning the generator on a list of bounding boxes and corresponding class labels. However, previous approaches are very restricti... | ['Andreas Dengel', 'Jörn Hees', 'Prateek Bansal', 'Stanislav Frolov'] | 2022-04-05 | null | null | null | null | ['conditional-image-generation'] | ['computer-vision'] | [ 6.77046120e-01 2.38627940e-01 -1.21188775e-01 -4.69633371e-01
-9.83741045e-01 -7.33266950e-01 8.93088698e-01 -3.41011345e-01
1.79147393e-01 9.16148126e-01 2.40192801e-01 -2.94636171e-02
2.52988249e-01 -9.74869490e-01 -9.59765494e-01 -5.36518574e-01
5.34922361e-01 4.45139587e-01 1.13908164e-01 -2.37422898... | [11.273294448852539, -0.25225284695625305] |
fbba435b-8ad1-452f-856f-921743c9a274 | community-detection-in-networks-a-user-guide | 1608.00163 | null | http://arxiv.org/abs/1608.00163v2 | http://arxiv.org/pdf/1608.00163v2.pdf | Community detection in networks: A user guide | Community detection in networks is one of the most popular topics of modern
network science. Communities, or clusters, are usually groups of vertices
having higher probability of being connected to each other than to members of
other groups, though other patterns are possible. Identifying communities is an
ill-defined ... | ['Darko Hric', 'Santo Fortunato'] | 2016-07-30 | null | null | null | null | ['misconceptions'] | ['miscellaneous'] | [ 2.18737368e-02 -2.79706493e-02 -1.02614544e-01 1.15639457e-04
2.70413846e-01 -8.20123255e-01 7.36565173e-01 5.52090406e-01
-1.25957668e-01 7.28599429e-01 -5.32218926e-02 -4.14114565e-01
-4.03328121e-01 -1.05260980e+00 -6.64747879e-02 -8.69798005e-01
-8.27559173e-01 7.87976980e-01 7.40688145e-01 -2.63111204... | [6.952313423156738, 5.298880577087402] |
1edbd283-2fc5-4f2f-8eaf-d1075bcfafe1 | mixed-attention-transformer-for | 2109.02789 | null | https://arxiv.org/abs/2109.02789v2 | https://arxiv.org/pdf/2109.02789v2.pdf | Mixed Attention Transformer for Leveraging Word-Level Knowledge to Neural Cross-Lingual Information Retrieval | Pretrained contextualized representations offer great success for many downstream tasks, including document ranking. The multilingual versions of such pretrained representations provide a possibility of jointly learning many languages with the same model. Although it is expected to gain big with such joint training, in... | ['James Allan', 'Razieh Rahimi', 'Sheikh Muhammad Sarwar', 'Hamed Bonab', 'Zhiqi Huang'] | 2021-09-07 | null | null | null | null | ['cross-lingual-information-retrieval'] | ['natural-language-processing'] | [-8.52297097e-02 -2.74008602e-01 -3.29373121e-01 -2.34161347e-01
-8.55371535e-01 -6.40238047e-01 7.47235239e-01 -7.82340392e-02
-5.63263714e-01 5.10145783e-01 5.82203329e-01 -4.38030660e-01
-8.97801667e-02 -7.77183890e-01 -9.53006446e-01 -5.23033619e-01
3.47034514e-01 7.36183882e-01 -1.29549444e-01 -6.01407886... | [11.369359016418457, 9.839285850524902] |
4f84ee31-4e67-47fe-9d7d-442903ae859e | perceptually-optimized-deep-high-dynamic | 2109.00180 | null | https://arxiv.org/abs/2109.00180v3 | https://arxiv.org/pdf/2109.00180v3.pdf | Perceptually Optimized Deep High-Dynamic-Range Image Tone Mapping | We describe a deep high-dynamic-range (HDR) image tone mapping operator that is computationally efficient and perceptually optimized. We first decompose an HDR image into a normalized Laplacian pyramid, and use two deep neural networks (DNNs) to estimate the Laplacian pyramid of the desired tone-mapped image from the n... | ['Kede Ma', 'Yuming Fang', 'Jiebin Yan', 'Chenyang Le'] | 2021-09-01 | null | null | null | null | ['tone-mapping'] | ['computer-vision'] | [ 3.76692474e-01 -2.27967188e-01 -2.41516560e-01 -3.43056947e-01
-9.96471226e-01 -3.58632386e-01 1.53056577e-01 -5.90416253e-01
-2.52870858e-01 4.26371127e-01 3.44260871e-01 9.93836764e-03
1.13797598e-01 -8.83375227e-01 -7.25386262e-01 -3.41066748e-01
4.41664830e-02 5.01243286e-02 3.39237690e-01 -3.83745939... | [10.980438232421875, -2.199376344680786] |
3d623f04-0932-4414-956f-5936f1a3b183 | improving-generalizability-of-graph-anomaly-1 | 2306.10534 | null | https://arxiv.org/abs/2306.10534v1 | https://arxiv.org/pdf/2306.10534v1.pdf | Improving Generalizability of Graph Anomaly Detection Models via Data Augmentation | Graph anomaly detection (GAD) is a vital task since even a few anomalies can pose huge threats to benign users. Recent semi-supervised GAD methods, which can effectively leverage the available labels as prior knowledge, have achieved superior performances than unsupervised methods. In practice, people usually need to i... | ['Long-Kai Huang', 'Fu-Lai Chung', 'Huachi Zhou', 'Ninghao Liu', 'Xiao Huang', 'Shuang Zhou'] | 2023-06-18 | null | null | null | null | ['graph-anomaly-detection', 'anomaly-detection'] | ['graphs', 'methodology'] | [ 2.01437756e-01 1.12497933e-01 -1.94702744e-01 -2.00957075e-01
-2.56201237e-01 -6.07878506e-01 4.51969951e-01 4.60455149e-01
1.33862086e-02 5.58336973e-01 -4.02757883e-01 -5.70949733e-01
-1.47159351e-02 -1.07256877e+00 -6.39440656e-01 -6.37265384e-01
-2.03841925e-03 2.66016096e-01 4.68311101e-01 -2.68530399... | [6.6421403884887695, 5.773899555206299] |
3fdf0276-aa46-44f4-8e5e-dda23434fe8f | learning-dialogue-representations-from | 2205.13568 | null | https://arxiv.org/abs/2205.13568v2 | https://arxiv.org/pdf/2205.13568v2.pdf | Learning Dialogue Representations from Consecutive Utterances | Learning high-quality dialogue representations is essential for solving a variety of dialogue-oriented tasks, especially considering that dialogue systems often suffer from data scarcity. In this paper, we introduce Dialogue Sentence Embedding (DSE), a self-supervised contrastive learning method that learns effective d... | ['Bing Xiang', 'Andrew O. Arnold', 'Xiaofei Ma', 'Nicholas Dingwall', 'Wei Xiao', 'Dejiao Zhang', 'Zhihan Zhou'] | 2022-05-26 | null | https://aclanthology.org/2022.naacl-main.55 | https://aclanthology.org/2022.naacl-main.55.pdf | naacl-2022-7 | ['dialogue-understanding', 'conversational-response-selection', 'dialogue-act-classification', 'goal-oriented-dialogue-systems', 'dialogue-management'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 2.83753276e-01 4.69964981e-01 -3.98005694e-01 -5.82283437e-01
-8.47436011e-01 -2.56161720e-01 1.28016889e+00 3.53650331e-01
-5.29738843e-01 7.91220427e-01 1.12892497e+00 -1.67413116e-01
1.40923038e-01 -6.76180363e-01 2.32075527e-01 -3.64266962e-01
3.43159260e-03 5.93924403e-01 1.54597268e-01 -1.05346787... | [12.703225135803223, 7.877967357635498] |
a4a36d7d-bc0b-4ea6-8969-770c3bd8b98c | detecting-overfitting-of-deep-generative | 1901.03396 | null | http://arxiv.org/abs/1901.03396v1 | http://arxiv.org/pdf/1901.03396v1.pdf | Detecting Overfitting of Deep Generative Networks via Latent Recovery | State of the art deep generative networks are capable of producing images
with such incredible realism that they can be suspected of memorizing training
images. It is why it is not uncommon to include visualizations of training set
nearest neighbors, to suggest generated images are not simply memorized. We
demonstrate ... | ['Julien Rabin', 'Loic Simon', 'Frederic Jurie', 'Ryan Webster'] | 2019-01-09 | detecting-overfitting-of-deep-generative-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Webster_Detecting_Overfitting_of_Deep_Generative_Networks_via_Latent_Recovery_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Webster_Detecting_Overfitting_of_Deep_Generative_Networks_via_Latent_Recovery_CVPR_2019_paper.pdf | cvpr-2019-6 | ['facial-inpainting'] | ['computer-vision'] | [ 3.29741329e-01 5.63803136e-01 5.14643729e-01 -7.19964579e-02
-5.42380393e-01 -4.04637516e-01 8.67457867e-01 -3.79049182e-01
-1.83117539e-01 1.18211663e+00 -5.03335744e-02 -5.41473851e-02
-5.13782492e-03 -1.03059030e+00 -9.40801561e-01 -8.26281190e-01
1.92110151e-01 4.91180748e-01 -3.16800654e-01 -3.73185158... | [12.050931930541992, -0.2565203607082367] |
9a403219-b619-4051-bc6e-61c72920b174 | group-wise-deep-co-saliency-detection | 1707.07381 | null | http://arxiv.org/abs/1707.07381v2 | http://arxiv.org/pdf/1707.07381v2.pdf | Group-wise Deep Co-saliency Detection | In this paper, we propose an end-to-end group-wise deep co-saliency detection
approach to address the co-salient object discovery problem based on the fully
convolutional network (FCN) with group input and group output. The proposed
approach captures the group-wise interaction information for group images by
learning a... | ['Xi Li', 'Omar El Farouk Bourahla', 'Fei Wu', 'Shanshan Zhao', 'Lina Wei'] | 2017-07-24 | null | null | null | null | ['co-saliency-detection'] | ['computer-vision'] | [ 2.56828696e-01 -2.32300926e-02 -6.26445115e-02 -5.90179741e-01
-5.45726895e-01 9.70340520e-02 4.28077251e-01 2.88175821e-01
-3.24257493e-01 1.21745147e-01 3.66685838e-01 2.32135117e-01
-4.84110892e-01 -5.38440943e-01 -9.11510110e-01 -4.37131941e-01
-2.67310381e-01 -6.57625347e-02 5.37905037e-01 5.36331795... | [9.826104164123535, -0.27957963943481445] |
697a4d55-f2bb-4945-b01e-a9ccd92f9377 | global-entity-disambiguation-with-bert | null | null | https://openreview.net/forum?id=STY-d6qQS9t | https://openreview.net/pdf?id=STY-d6qQS9t | Global Entity Disambiguation with BERT | We propose a global entity disambiguation (ED) model based on BERT. To capture global contextual information for ED, our model treats not only words but also entities as input tokens, and solves the task by sequentially resolving mentions to their referent entities and using resolved entities as inputs. We train the mo... | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['entity-disambiguation'] | ['natural-language-processing'] | [-8.61033142e-01 4.39337701e-01 -4.49943155e-01 -2.54222900e-01
-7.98948765e-01 -8.12534392e-01 7.93870151e-01 6.83396041e-01
-1.34103346e+00 1.12926555e+00 5.47145486e-01 4.06878777e-02
6.26352951e-02 -7.95154512e-01 -4.48783785e-01 9.01983008e-02
-2.77209044e-01 9.10542488e-01 5.41400969e-01 -4.90868121... | [9.502453804016113, 8.966890335083008] |
b95b9c51-2d64-480f-a168-c18f07b29801 | optical-remote-sensing-image-understanding | 2204.09120 | null | https://arxiv.org/abs/2204.09120v1 | https://arxiv.org/pdf/2204.09120v1.pdf | Optical Remote Sensing Image Understanding with Weak Supervision: Concepts, Methods, and Perspectives | In recent years, supervised learning has been widely used in various tasks of optical remote sensing image understanding, including remote sensing image classification, pixel-wise segmentation, change detection, and object detection. The methods based on supervised learning need a large amount of high-quality training ... | ['Antonio J Plaza', 'Jocelyn Chanussot', 'Jun Li', 'Weiying Xie', 'Pedram Ghamisi', 'Leyuan Fang', 'Jun Yue'] | 2022-04-18 | null | null | null | null | ['remote-sensing-image-classification'] | ['miscellaneous'] | [ 6.27971828e-01 -3.46630096e-01 -3.41305345e-01 -4.92787093e-01
-5.34971297e-01 -5.66647828e-01 2.87012815e-01 2.89182007e-01
-4.01405841e-01 9.54676270e-01 -2.79625833e-01 -3.73031169e-01
-1.98966518e-01 -1.06707478e+00 -4.87921327e-01 -1.02535248e+00
3.45517576e-01 2.60033935e-01 1.67455837e-01 -6.95599765... | [9.70629596710205, -1.3549489974975586] |
7922dc80-a507-45f7-80e0-72c40744d4dc | viscoelastic-constitutive-artificial-neural | 2303.12164 | null | https://arxiv.org/abs/2303.12164v1 | https://arxiv.org/pdf/2303.12164v1.pdf | Viscoelastic Constitutive Artificial Neural Networks (vCANNs) $-$ a framework for data-driven anisotropic nonlinear finite viscoelasticity | The constitutive behavior of polymeric materials is often modeled by finite linear viscoelastic (FLV) or quasi-linear viscoelastic (QLV) models. These popular models are simplifications that typically cannot accurately capture the nonlinear viscoelastic behavior of materials. For example, the success of attempts to cap... | ['Christian J. Cyron', 'Kevin Linka', 'Kian P. Abdolazizi'] | 2023-03-21 | null | null | null | null | ['physics-informed-machine-learning'] | ['graphs'] | [ 2.6737475e-01 -1.0445893e-01 -2.7918616e-01 4.2646300e-02
6.7631342e-02 -3.1133074e-01 -4.1206971e-02 5.8154162e-02
-3.5136321e-01 9.2304301e-01 -1.6401831e-02 -1.8346900e-01
-6.1424673e-01 -5.9273547e-01 -1.0025958e+00 -8.8603222e-01
-5.3274876e-01 6.0027045e-01 3.6253995e-01 -1.3576323e-01
1.4195972e-02... | [6.374929428100586, 3.426564931869507] |
74ab2158-f985-4c45-a44a-734029e7642e | hqdec-self-supervised-monocular-depth | 2305.18706 | null | https://arxiv.org/abs/2305.18706v1 | https://arxiv.org/pdf/2305.18706v1.pdf | HQDec: Self-Supervised Monocular Depth Estimation Based on a High-Quality Decoder | Decoders play significant roles in recovering scene depths. However, the decoders used in previous works ignore the propagation of multilevel lossless fine-grained information, cannot adaptively capture local and global information in parallel, and cannot perform sufficient global statistical analyses on the final outp... | ['Jun Cheng', 'Fei Wang'] | 2023-05-30 | null | null | null | null | ['monocular-depth-estimation'] | ['computer-vision'] | [ 1.27593651e-01 -1.83194816e-01 -1.43552542e-01 -4.71708238e-01
-1.28156149e+00 -1.98842101e-02 3.19438607e-01 -8.88223797e-02
-4.21195626e-01 8.18800807e-01 4.03710246e-01 3.53296816e-01
-1.98247790e-01 -1.08741021e+00 -8.71238470e-01 -9.12635982e-01
2.52769440e-01 1.44984007e-01 5.32828212e-01 4.77750972... | [9.382522583007812, -2.495950222015381] |
07158f45-aeab-4a2c-a7ba-82da6d820781 | geometric-dimensionality-reduction-for | 1709.01233 | null | https://arxiv.org/abs/1709.01233v9 | https://arxiv.org/pdf/1709.01233v9.pdf | Supervised Dimensionality Reduction for Big Data | To solve key biomedical problems, experimentalists now routinely measure millions or billions of features (dimensions) per sample, with the hope that data science techniques will be able to build accurate data-driven inferences. Because sample sizes are typically orders of magnitude smaller than the dimensionality of t... | ['Christopher Douville', 'Minh Tang', 'Joshua T. Vogelstein', 'Mauro Maggioni', 'Eric Bridgeford', 'Da Zheng', 'Randal Burns'] | 2017-09-05 | null | null | null | null | ['supervised-dimensionality-reduction'] | ['computer-vision'] | [ 3.18282843e-01 -7.77685270e-02 -2.22973093e-01 -6.39507473e-01
-7.54728973e-01 -4.87042785e-01 5.97602367e-01 2.80182064e-01
-3.29589933e-01 7.65314996e-01 5.04250586e-01 -1.74516961e-01
-6.38147950e-01 -6.94502413e-01 -4.34463501e-01 -7.26394415e-01
-2.97761798e-01 6.36445522e-01 -4.74758923e-01 3.19535047... | [7.2881855964660645, 4.852302074432373] |
54eb000a-9f7c-45c3-a3c3-801aef003867 | classification-of-lung-nodules-in-ct-images | 1807.00094 | null | http://arxiv.org/abs/1807.00094v1 | http://arxiv.org/pdf/1807.00094v1.pdf | Classification of lung nodules in CT images based on Wasserstein distance in differential geometry | Lung nodules are commonly detected in screening for patients with a risk for
lung cancer. Though the status of large nodules can be easily diagnosed by fine
needle biopsy or bronchoscopy, small nodules are often difficult to classify on
computed tomography (CT). Recent works have shown that shape analysis of lung
nodul... | ['Xianfeng GU', 'Deruo Liu', 'Qianli Ma', 'Xiaoyin Xu', 'Chengfeng Wen', 'Min Zhang', 'Hai Chen', 'Jie He'] | 2018-06-30 | null | null | null | null | ['lung-nodule-classification'] | ['medical'] | [-1.34043023e-02 2.52657354e-01 -5.77341095e-02 -5.78212412e-03
-3.51916105e-01 -6.70508087e-01 4.99822706e-01 2.28696037e-02
-3.07248980e-01 2.74645329e-01 -2.87122846e-01 -5.24951875e-01
-2.85158277e-01 -9.71551597e-01 -2.11592510e-01 -8.76907885e-01
1.42978832e-01 9.65604961e-01 1.05697823e+00 7.80041441... | [15.306270599365234, -2.1898996829986572] |
16170bb9-8f92-471a-b95e-569dea0d6790 | sensor-fusion-using-emg-and-vision-for-hand | 1910.11126 | null | https://arxiv.org/abs/1910.11126v1 | https://arxiv.org/pdf/1910.11126v1.pdf | Sensor fusion using EMG and vision for hand gesture classification in mobile applications | The discrimination of human gestures using wearable solutions is extremely important as a supporting technique for assisted living, healthcare of the elderly and neurorehabilitation. This paper presents a mobile electromyography (EMG) analysis framework to be an auxiliary component in physiotherapy sessions or as a fee... | ['Elisa Donati', 'Melika Payvand', 'Lyes Khacef', 'Gemma Taverni', 'Enea Ceolini'] | 2019-10-19 | null | null | null | null | ['electromyography-emg'] | ['medical'] | [ 3.04853112e-01 -5.51282354e-02 1.38586042e-02 -1.47080645e-01
-8.06369126e-01 -2.29765698e-01 1.47696018e-01 -3.04326952e-01
-9.06671703e-01 7.02594280e-01 3.72654706e-01 1.22246280e-01
-3.74613851e-01 -3.37613612e-01 -4.80787545e-01 -7.76695728e-01
7.66309723e-02 6.35473669e-01 2.14063302e-01 9.36275348... | [6.865433692932129, 0.2123619168996811] |
cc738096-b64f-4e15-99f1-b60c19d3db23 | discussion-paper-the-threat-of-real-time | 2306.02487 | null | https://arxiv.org/abs/2306.02487v1 | https://arxiv.org/pdf/2306.02487v1.pdf | Discussion Paper: The Threat of Real Time Deepfakes | Generative deep learning models are able to create realistic audio and video. This technology has been used to impersonate the faces and voices of individuals. These ``deepfakes'' are being used to spread misinformation, enable scams, perform fraud, and blackmail the innocent. The technology continues to advance and to... | ['Yisroel Mirsky', 'Guy Frankovits'] | 2023-06-04 | null | null | null | null | ['misinformation'] | ['miscellaneous'] | [ 2.42906548e-02 1.43437311e-01 3.44142973e-01 1.89419866e-01
-4.80892062e-01 -8.83235991e-01 8.64473581e-01 -1.96666002e-01
-3.65380228e-01 7.51443207e-01 2.97033548e-01 -3.92669201e-01
2.44579285e-01 -9.27945316e-01 -3.09712350e-01 -5.35638571e-01
-2.69139290e-01 1.10797761e-02 -1.00906841e-01 -4.04575527... | [6.189533710479736, 7.748828887939453] |
0aa9987a-2d8b-43e6-939a-aa1886b47f87 | acvnet-attention-concatenation-volume-for | 2203.02146 | null | https://arxiv.org/abs/2203.02146v3 | https://arxiv.org/pdf/2203.02146v3.pdf | Attention Concatenation Volume for Accurate and Efficient Stereo Matching | Stereo matching is a fundamental building block for many vision and robotics applications. An informative and concise cost volume representation is vital for stereo matching of high accuracy and efficiency. In this paper, we present a novel cost volume construction method which generates attention weights from correlat... | ['Xin Yang', 'Peng Guo', 'Junda Cheng', 'Gangwei Xu'] | 2022-03-04 | attention-concatenation-volume-for-accurate | http://openaccess.thecvf.com//content/CVPR2022/html/Xu_Attention_Concatenation_Volume_for_Accurate_and_Efficient_Stereo_Matching_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Xu_Attention_Concatenation_Volume_for_Accurate_and_Efficient_Stereo_Matching_CVPR_2022_paper.pdf | cvpr-2022-1 | ['stereo-depth-estimation', 'patch-matching', 'stereo-matching-1'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 2.47165024e-01 -1.96872503e-01 -1.56761277e-02 -2.46103749e-01
-4.58290368e-01 2.10678186e-02 2.99868762e-01 -7.31211603e-02
-3.35804522e-01 4.12657827e-01 3.47199112e-01 3.49634662e-02
-2.80013889e-01 -9.82946098e-01 -6.75971687e-01 -5.86975574e-01
3.86719942e-01 4.90421653e-02 6.12965345e-01 -3.93505931... | [8.853891372680664, -2.226198196411133] |
32fe6e03-04ce-4a76-a39b-b87dc982fd9b | video-object-detection-for-privacy-preserving | 2306.14620 | null | https://arxiv.org/abs/2306.14620v1 | https://arxiv.org/pdf/2306.14620v1.pdf | Video object detection for privacy-preserving patient monitoring in intensive care | Patient monitoring in intensive care units, although assisted by biosensors, needs continuous supervision of staff. To reduce the burden on staff members, IT infrastructures are built to record monitoring data and develop clinical decision support systems. These systems, however, are vulnerable to artifacts (e.g. muscl... | ['Thilo Stadelmann', 'Emanuela Keller', 'Lukas Tuggener', 'Shufan Huo', 'Marko Seric', 'Daniel Baumann', 'Jens Michael Boss', 'Raphael Emberger'] | 2023-06-26 | null | null | null | null | ['video-object-detection'] | ['computer-vision'] | [ 5.42202830e-01 -7.85099193e-02 6.77222908e-02 -2.48156227e-02
-2.48841465e-01 -4.70161110e-01 -2.03230102e-02 4.45493102e-01
-6.29483461e-01 7.54180789e-01 -2.94625163e-01 -1.18764155e-01
-5.01900539e-02 -4.68924403e-01 -5.24357855e-01 -8.02847862e-01
-3.59707534e-01 -9.90895480e-02 4.45639402e-01 3.44732940... | [13.874136924743652, 2.835064649581909] |
8395dd55-d037-497c-a64f-ca725df9a3d2 | skeletor-skeletal-transformers-for-robust | 2104.11712 | null | https://arxiv.org/abs/2104.11712v1 | https://arxiv.org/pdf/2104.11712v1.pdf | Skeletor: Skeletal Transformers for Robust Body-Pose Estimation | Predicting 3D human pose from a single monoscopic video can be highly challenging due to factors such as low resolution, motion blur and occlusion, in addition to the fundamental ambiguity in estimating 3D from 2D. Approaches that directly regress the 3D pose from independent images can be particularly susceptible to t... | ['Richard Bowden', 'Necati Cihan Camgoz', 'Tao Jiang'] | 2021-04-23 | null | null | null | null | ['sign-language-translation'] | ['computer-vision'] | [ 1.59171879e-01 -3.11804354e-01 -4.62949127e-02 -3.28673571e-01
-7.76884019e-01 -4.57532883e-01 4.66563940e-01 -5.99273682e-01
-6.21708632e-01 6.32823050e-01 5.91343522e-01 3.74740720e-01
1.52351111e-01 -1.89653948e-01 -8.50336015e-01 -5.09540856e-01
5.75936362e-02 5.95057249e-01 5.15050173e-01 -3.96103673... | [7.155967712402344, -0.776003897190094] |
16e64e2b-cdab-4897-a4e0-8d6f418555f2 | point2point-a-framework-for-efficient-deep | 2306.16306 | null | https://arxiv.org/abs/2306.16306v1 | https://arxiv.org/pdf/2306.16306v1.pdf | Point2Point : A Framework for Efficient Deep Learning on Hilbert sorted Point Clouds with applications in Spatio-Temporal Occupancy Prediction | The irregularity and permutation invariance of point cloud data pose challenges for effective learning. Conventional methods for addressing this issue involve converting raw point clouds to intermediate representations such as 3D voxel grids or range images. While such intermediate representations solve the problem of ... | ['Athrva Atul Pandhare'] | 2023-06-28 | null | null | null | null | ['point-cloud-segmentation'] | ['computer-vision'] | [ 2.09652677e-01 -7.72921368e-02 -2.11143315e-01 -4.98851389e-01
-1.11934972e+00 -6.55988395e-01 5.94395459e-01 3.81513059e-01
-9.44034606e-02 4.92245108e-01 -1.61600575e-01 -5.36830544e-01
-4.17422950e-01 -9.48667705e-01 -1.29127204e+00 -4.34636444e-01
-4.25458878e-01 1.04428220e+00 2.46920004e-01 1.29896536... | [8.00989818572998, -3.4988503456115723] |
0c616553-8ce5-45b4-8440-0ef5d1cb56af | hyperspectral-image-analysis-with-subspace | 2304.09730 | null | https://arxiv.org/abs/2304.09730v1 | https://arxiv.org/pdf/2304.09730v1.pdf | Hyperspectral Image Analysis with Subspace Learning-based One-Class Classification | Hyperspectral image (HSI) classification is an important task in many applications, such as environmental monitoring, medical imaging, and land use/land cover (LULC) classification. Due to the significant amount of spectral information from recent HSI sensors, analyzing the acquired images is challenging using traditio... | ['Moncef Gabbouj', 'Turker Ince', 'Fahad Sohrab', 'Mete Ahishali', 'Sertac Kilickaya'] | 2023-04-19 | null | null | null | null | ['one-class-classification'] | ['miscellaneous'] | [ 7.97424734e-01 -3.81575167e-01 -3.03720742e-01 -2.59291530e-01
-7.26606190e-01 -5.44470251e-01 4.07900929e-01 2.16278851e-01
-2.72214890e-01 7.18364298e-01 -2.20629022e-01 -2.31367454e-01
-4.94542480e-01 -8.02369952e-01 -2.34175831e-01 -1.21615899e+00
1.25199603e-02 2.22160622e-01 -2.46663406e-01 4.69776914... | [10.00133991241455, -1.8804876804351807] |
d5c9b365-d69b-424e-88ac-ccb89a26abe1 | bidirectional-feature-pyramid-network-with | null | null | http://openaccess.thecvf.com/content_ECCV_2018/html/Lei_Zhu_Bi-directional_Feature_Pyramid_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Lei_Zhu_Bi-directional_Feature_Pyramid_ECCV_2018_paper.pdf | Bidirectional Feature Pyramid Network with Recurrent Attention Residual Modules for Shadow Detection | This paper presents a network to detect shadows by exploring and combining global context in deep layers and local context in shallow layers of a deep convolutional neural network (CNN). There are two technical contributions in our network design. First, we formulate the recurrent attention residual (RAR) module to com... | ['Xiao-Wei Hu', 'Pheng-Ann Heng', 'Chi-Wing Fu', 'Zijun Deng', 'Lei Zhu', 'Xuemiao Xu', 'Jing Qin'] | 2018-09-01 | null | null | null | eccv-2018-9 | ['shadow-detection'] | ['computer-vision'] | [ 5.75835884e-01 1.73610337e-02 2.35267669e-01 -5.05198598e-01
-5.24072945e-01 2.15848032e-02 2.29333758e-01 -4.59542662e-01
-2.76082996e-02 6.56138480e-01 3.88718575e-01 -3.65540534e-01
3.80201608e-01 -7.10322618e-01 -7.21364260e-01 -7.66627550e-01
-5.74515127e-02 -4.85376596e-01 9.84450936e-01 -2.62838185... | [10.863597869873047, -4.128228664398193] |
f66e27f8-a932-452c-a9ff-7e5825d842e1 | tracking-deformable-parts-via-dynamic | 1311.0262 | null | http://arxiv.org/abs/1311.0262v1 | http://arxiv.org/pdf/1311.0262v1.pdf | Tracking Deformable Parts via Dynamic Conditional Random Fields | Despite the success of many advanced tracking methods in this area, tracking
targets with drastic variation of appearance such as deformation, view change
and partial occlusion in video sequences is still a challenge in practical
applications. In this letter, we take these serious tracking problems into
account simulta... | ['Zhixin Sun', 'Xu Cheng', 'Zhenyang Wu', 'Suofei Zhang'] | 2013-10-30 | null | null | null | null | ['occlusion-handling'] | ['computer-vision'] | [-1.39450356e-01 -5.69518745e-01 -1.97817251e-01 -7.14804679e-02
-3.71788800e-01 -7.31964052e-01 5.07777035e-01 -4.41901237e-01
-2.74376988e-01 6.51502490e-01 -2.87677497e-01 1.95173845e-01
-5.67547046e-02 -3.23814541e-01 -6.76098466e-01 -8.49438429e-01
-6.92427829e-02 3.76982063e-01 9.83963609e-01 1.45894572... | [6.312592029571533, -2.1886115074157715] |
9fd100da-2e44-4bcd-a140-098e3e821123 | covid-19-diagnosis-from-x-ray-using-neural | 2105.14333 | null | https://arxiv.org/abs/2105.14333v1 | https://arxiv.org/pdf/2105.14333v1.pdf | Covid-19 diagnosis from x-ray using neural networks | Corona virus or COVID-19 is a pandemic illness, which has influenced more than million of causalities worldwide and infected a few large number of individuals .Innovative instrument empowering quick screening of the COVID-19 contamination with high precision can be critically useful to the medical care experts. The pri... | ['Mohammed Rhithick A', 'Dinesh J'] | 2021-05-29 | null | null | null | null | ['covid-19-detection'] | ['medical'] | [ 3.32975835e-01 -1.96497262e-01 2.16968715e-01 -5.85512072e-02
-1.49417281e-01 -6.63324893e-01 1.60267532e-01 3.08420688e-01
-6.00570917e-01 7.42070913e-01 -1.39781937e-01 -9.56160605e-01
-4.38209444e-01 -7.30784416e-01 -3.98944765e-01 -6.54445291e-01
-8.34162012e-02 1.05237639e+00 -2.59673297e-01 -3.02319318... | [15.581006050109863, -1.6898729801177979] |
52cc5946-26de-4fc6-992c-1c812cdbc2bc | improving-low-resource-cross-lingual-document | 1906.03492 | null | https://arxiv.org/abs/1906.03492v1 | https://arxiv.org/pdf/1906.03492v1.pdf | Improving Low-Resource Cross-lingual Document Retrieval by Reranking with Deep Bilingual Representations | In this paper, we propose to boost low-resource cross-lingual document retrieval performance with deep bilingual query-document representations. We match queries and documents in both source and target languages with four components, each of which is implemented as a term interaction-based deep neural network with cros... | ['Neha Verma', 'Sungrok Shim', 'Rui Zhang', 'Garrett Bingham', 'Caitlin Westerfield', 'William Hu', 'Dragomir Radev', 'Alexander Fabbri'] | 2019-06-08 | improving-low-resource-cross-lingual-document-1 | https://aclanthology.org/P19-1306 | https://aclanthology.org/P19-1306.pdf | acl-2019-7 | ['cross-lingual-information-retrieval'] | ['natural-language-processing'] | [-3.18737984e-01 -4.59747225e-01 -7.40697443e-01 -3.58497530e-01
-1.78610158e+00 -8.14129293e-01 9.73609209e-01 1.92235366e-01
-1.01866257e+00 4.62723047e-01 5.91360390e-01 -4.25963193e-01
-1.67653143e-01 -5.82506955e-01 -6.03758454e-01 -3.15344930e-01
2.41253912e-01 1.16239417e+00 1.54009880e-02 -4.57190841... | [11.378799438476562, 9.810039520263672] |
cffb8cb1-2bed-4e0c-afb9-839436863dd8 | fine-grained-visual-classification-via-2 | null | null | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10042971 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10042971 | Fine-Grained Visual Classification via Internal Ensemble Learning Transformer | Recently, vision transformers (ViTs) have been investigated in fine-grained visual recognition (FGVC) and are now
considered state of the art. However, most ViT-based works ignore
the different learning performances of the heads in the multihead self-attention (MHSA) mechanism and its layers. To address
these issues... | ['Bin Luo', 'Bo Jiang', 'Jiahui Wang', 'Qin Xu'] | 2023-02-13 | null | null | null | ieee-transactions-on-multimedia-2023-2 | ['fine-grained-visual-recognition', 'fine-grained-image-classification'] | ['computer-vision', 'computer-vision'] | [-1.11182585e-01 -3.52590799e-01 3.21714655e-02 -2.71616548e-01
-4.29593623e-01 -9.06101540e-02 4.92971867e-01 -2.04338804e-01
-5.55747926e-01 6.35800660e-01 3.04066300e-01 5.82468770e-02
-9.80847180e-02 -7.02775896e-01 -5.07908106e-01 -1.12742698e+00
5.09159863e-01 -1.58170182e-02 6.50681734e-01 -3.14685591... | [9.641074180603027, 1.9649802446365356] |
bff85f59-7c1a-4c5c-8c27-1a6a537ba0d9 | doc2graph-a-task-agnostic-document | 2208.11168 | null | https://arxiv.org/abs/2208.11168v1 | https://arxiv.org/pdf/2208.11168v1.pdf | Doc2Graph: a Task Agnostic Document Understanding Framework based on Graph Neural Networks | Geometric Deep Learning has recently attracted significant interest in a wide range of machine learning fields, including document analysis. The application of Graph Neural Networks (GNNs) has become crucial in various document-related tasks since they can unravel important structural patterns, fundamental in key infor... | ['Simone Marinai', 'Josep Lladós', 'Enrico Civitelli', 'Sanket Biswas', 'Andrea Gemelli'] | 2022-08-23 | null | null | null | null | ['document-layout-analysis', 'table-detection', 'semantic-entity-labeling', 'key-information-extraction'] | ['computer-vision', 'miscellaneous', 'natural-language-processing', 'natural-language-processing'] | [ 1.38751611e-01 7.95909092e-02 -2.40284085e-01 -1.63386986e-01
-1.90294832e-01 -9.82522726e-01 7.72908866e-01 6.76869631e-01
-1.41900796e-02 2.18299791e-01 2.76386976e-01 -7.34791338e-01
-5.93553424e-01 -1.24391079e+00 -6.78315341e-01 -1.49695054e-01
6.26227930e-02 7.13911176e-01 -1.35664195e-02 -3.30653965... | [11.663901329040527, 2.811992645263672] |
fe0ad60c-e0a0-4474-bf8b-156e78479e06 | tensor-networks-for-high-order-polynomial | 2204.07743 | null | https://arxiv.org/abs/2204.07743v1 | https://arxiv.org/pdf/2204.07743v1.pdf | Tensor-networks for High-order Polynomial Approximation: A Many-body Physics Perspective | We analyze the problem of high-order polynomial approximation from a many-body physics perspective, and demonstrate the descriptive power of entanglement entropy in capturing model capacity and task complexity. Instantiated with a high-order nonlinear dynamics modeling problem, tensor-network models are investigated an... | ['Tong Yang'] | 2022-04-16 | null | null | null | null | ['tensor-networks'] | ['methodology'] | [-1.23546757e-01 8.93888324e-02 -1.85665622e-01 -2.45031845e-02
-1.14736699e-01 -3.53108585e-01 7.23709464e-01 -2.20092565e-01
-2.59723276e-01 5.41384876e-01 1.74829409e-01 -1.57476872e-01
-8.03632557e-01 -5.07709563e-01 -4.17006791e-01 -7.34806836e-01
-7.70547569e-01 4.58189875e-01 -2.26164192e-01 -4.70815688... | [5.701115608215332, 4.987032890319824] |
2dc23d08-7501-4d9d-9ea4-237beaec4481 | learning-to-forecast-vegetation-greenness-at | 2210.13648 | null | https://arxiv.org/abs/2210.13648v2 | https://arxiv.org/pdf/2210.13648v2.pdf | Learning to forecast vegetation greenness at fine resolution over Africa with ConvLSTMs | Forecasting the state of vegetation in response to climate and weather events is a major challenge. Its implementation will prove crucial in predicting crop yield, forest damage, or more generally the impact on ecosystems services relevant for socio-economic functioning, which if absent can lead to humanitarian disaste... | ['Markus Reichstein', 'Nuno Carvalhais', 'Jeran Poehls', 'Lazaro Alonso', 'Vitus Benson', 'Christian Requena-Mesa', 'Claire Robin'] | 2022-10-24 | null | null | null | null | ['video-prediction'] | ['computer-vision'] | [ 4.03399289e-01 -3.69218767e-01 -4.03663665e-02 -7.86605105e-02
1.55878350e-01 -7.66685069e-01 7.45170474e-01 2.34311596e-01
-3.05097818e-01 9.67369676e-01 3.50562155e-01 -8.22444975e-01
-1.95428446e-01 -1.43903291e+00 -4.99354064e-01 -8.92610669e-01
-8.10729861e-01 -7.17074424e-02 8.19660649e-02 -1.01685798... | [9.509114265441895, -1.5604009628295898] |
f6d08d7d-28a7-4d88-aefc-f6caba42d212 | explainable-verbal-deception-detection-using | 2210.03080 | null | https://arxiv.org/abs/2210.03080v1 | https://arxiv.org/pdf/2210.03080v1.pdf | Explainable Verbal Deception Detection using Transformers | People are regularly confronted with potentially deceptive statements (e.g., fake news, misleading product reviews, or lies about activities). Only few works on automated text-based deception detection have exploited the potential of deep learning approaches. A critique of deep-learning methods is their lack of interpr... | ['Bennett Kleinberg', 'Felix Soldner', 'Loukas Ilias'] | 2022-10-06 | null | null | null | null | ['deception-detection'] | ['miscellaneous'] | [-1.00792602e-01 -2.26579271e-02 -2.49929875e-01 -5.86423218e-01
-4.79345709e-01 -5.53903043e-01 9.40528452e-01 1.82261780e-01
-2.27023840e-01 6.28899992e-01 4.59407002e-01 -6.48586690e-01
4.46914621e-02 -3.83349687e-01 -3.67694050e-01 -3.73313636e-01
2.27125168e-01 1.91876069e-01 -4.20589000e-01 -3.61729681... | [8.09903335571289, 10.295369148254395] |
b5ffdf49-f01e-4d74-84f1-f2d3883a410f | mantra-net-manipulation-tracing-network-for | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Wu_ManTra-Net_Manipulation_Tracing_Network_for_Detection_and_Localization_of_Image_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Wu_ManTra-Net_Manipulation_Tracing_Network_for_Detection_and_Localization_of_Image_CVPR_2019_paper.pdf | ManTra-Net: Manipulation Tracing Network for Detection and Localization of Image Forgeries With Anomalous Features | To fight against real-life image forgery, which commonly involves different types and combined manipulations, we propose a unified deep neural architecture called ManTra-Net. Unlike many existing solutions, ManTra-Net is an end-to-end network that performs both detection and localization without extra preprocessing... | [' Premkumar Natarajan', ' Wael AbdAlmageed', 'Yue Wu'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['image-forensics', 'fake-image-detection'] | ['computer-vision', 'computer-vision'] | [ 2.80995429e-01 -7.17891574e-01 1.51277825e-01 -2.91317344e-01
-6.64881229e-01 -5.19280195e-01 3.20607305e-01 -9.71943438e-02
-2.40329862e-01 2.43165955e-01 -5.38431183e-02 -3.92813981e-01
-2.26058848e-02 -6.17101490e-01 -8.99093688e-01 -5.91835797e-01
-3.10940355e-01 -4.16463077e-01 1.12398207e-01 -2.63926268... | [12.333465576171875, 0.9579513669013977] |
b1738197-f29c-40ba-a0cf-d334b0c72852 | a-comprehensive-survey-on-deep-music | 2011.06801 | null | https://arxiv.org/abs/2011.06801v1 | https://arxiv.org/pdf/2011.06801v1.pdf | A Comprehensive Survey on Deep Music Generation: Multi-level Representations, Algorithms, Evaluations, and Future Directions | The utilization of deep learning techniques in generating various contents (such as image, text, etc.) has become a trend. Especially music, the topic of this paper, has attracted widespread attention of countless researchers.The whole process of producing music can be divided into three stages, corresponding to the th... | ['Xinyu Yang', 'Jing Luo', 'Shulei Ji'] | 2020-11-13 | null | null | null | null | ['audio-generation'] | ['audio'] | [ 4.15330917e-01 -3.06371957e-01 8.28815624e-02 1.76691964e-01
-7.50439644e-01 -7.29696393e-01 5.49778879e-01 -4.24385428e-01
1.98084936e-01 7.27603972e-01 5.55352509e-01 3.89355749e-01
-4.65603262e-01 -8.53909910e-01 -3.08286965e-01 -8.99604440e-01
6.83063418e-02 3.82217228e-01 -3.96335632e-01 -3.56836200... | [16.062641143798828, 5.538880348205566] |
3e38a948-ab0c-4ba3-850d-3c3eb99b1a91 | plan2scene-converting-floorplans-to-3d-scenes | 2106.05375 | null | https://arxiv.org/abs/2106.05375v1 | https://arxiv.org/pdf/2106.05375v1.pdf | Plan2Scene: Converting Floorplans to 3D Scenes | We address the task of converting a floorplan and a set of associated photos of a residence into a textured 3D mesh model, a task which we call Plan2Scene. Our system 1) lifts a floorplan image to a 3D mesh model; 2) synthesizes surface textures based on the input photos; and 3) infers textures for unobserved surfaces ... | ['Manolis Savva', 'Angel X. Chang', 'Yasutaka Furukawa', 'Qirui Wu', 'Madhawa Vidanapathirana'] | 2021-06-09 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Vidanapathirana_Plan2Scene_Converting_Floorplans_to_3D_Scenes_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Vidanapathirana_Plan2Scene_Converting_Floorplans_to_3D_Scenes_CVPR_2021_paper.pdf | cvpr-2021-1 | ['plan2scene'] | ['computer-vision'] | [ 1.05460846e+00 6.93105519e-01 3.55670422e-01 -5.91718674e-01
-8.42821121e-01 -6.66855752e-01 4.57231760e-01 9.43402424e-02
6.38051748e-01 4.54442799e-01 4.35382456e-01 -2.23589346e-01
2.30205595e-01 -1.41434312e+00 -1.16104507e+00 -1.29496872e-01
-4.74088043e-02 6.94170296e-01 6.67350367e-02 -2.53112465... | [9.265108108520508, -3.0721046924591064] |
b4d93157-d194-43fa-9576-1a3ff3f37608 | guard-graph-universal-adversarial-defense | 2204.09803 | null | https://arxiv.org/abs/2204.09803v3 | https://arxiv.org/pdf/2204.09803v3.pdf | GUARD: Graph Universal Adversarial Defense | Graph convolutional networks (GCNs) have shown to be vulnerable to small adversarial perturbations, which becomes a severe threat and largely limits their applications in security-critical scenarios. To mitigate such a threat, considerable research efforts have been devoted to increasing the robustness of GCNs against ... | ['Jiawang Dan', 'Weiqiang Wang', 'Zibin Zheng', 'Changhua Meng', 'Liang Chen', 'Ruofan Wu', 'Jie Liao', 'Jintang Li'] | 2022-04-20 | null | null | null | null | ['adversarial-defense'] | ['adversarial'] | [ 1.83076859e-01 3.31813693e-01 -2.01200560e-01 8.33968073e-02
-5.70006073e-01 -1.32362092e+00 6.10902071e-01 8.46394077e-02
-1.72162743e-03 4.98279363e-01 -7.91374967e-03 -8.03358555e-01
5.45148551e-02 -1.09848630e+00 -7.13725388e-01 -8.10862362e-01
-4.51658458e-01 -1.62910387e-01 5.98100603e-01 -6.43938363... | [6.108344078063965, 7.296267986297607] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.