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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
564547e4-7c95-41d3-bb95-da6e42abdf99 | risk-aware-and-multi-objective-decision | 2102.00966 | null | https://arxiv.org/abs/2102.00966v2 | https://arxiv.org/pdf/2102.00966v2.pdf | Risk Aware and Multi-Objective Decision Making with Distributional Monte Carlo Tree Search | In many risk-aware and multi-objective reinforcement learning settings, the utility of the user is derived from the single execution of a policy. In these settings, making decisions based on the average future returns is not suitable. For example, in a medical setting a patient may only have one opportunity to treat th... | ['Patrick Mannion', 'Enda Howley', 'Diederik M. Roijers', 'Mathieu Reymond', 'Conor F. Hayes'] | 2021-02-01 | null | null | null | null | ['multi-objective-reinforcement-learning'] | ['methodology'] | [ 7.15171248e-02 3.50658834e-01 -7.57763386e-01 -2.18464494e-01
-7.82559931e-01 -4.02305722e-01 2.43674308e-01 5.53814113e-01
-8.48378718e-01 1.27801633e+00 1.60977811e-01 -6.19536698e-01
-6.15127027e-01 -1.17262518e+00 -4.52577889e-01 -7.32312441e-01
-4.09597248e-01 9.10211861e-01 -1.93005294e-01 1.41072676... | [4.239803791046143, 2.644524335861206] |
3efd8ecc-056e-48b9-ae2a-c2b1290a4cd6 | can-a-simple-approach-identify-complex-nurse | null | null | https://doi.org/10.1145/3341162.3344859 | http://delivery.acm.org/10.1145/3350000/3344859/p736-kadir.pdf | Can a simple approach identify complex nurse care activity? | For the last two decades, more and more complex methods have been developed to identify human activities using various types of sensors, e.g., data from motion capture, accelerometer, and gyroscopes sensors. To date, most of the researches mainly focus on identifying simple human activities, e.g., walking, eating, and ... | ['Sadia Sharmin', 'Mohammad Shoyaib', 'Md. Eusha Kadir', 'Pritom Saha Akash', 'Amin Ahsan Ali'] | 2019-09-09 | null | null | null | ubicompiswc-19-proceedings-of-the-2019-acm | ['multimodal-activity-recognition'] | ['computer-vision'] | [ 3.21957558e-01 -2.02821746e-01 -5.77717185e-01 -3.70920420e-01
-2.30042741e-01 -1.28640711e-01 2.03684688e-01 4.33550060e-01
-4.82881963e-01 8.75819445e-01 5.87274790e-01 -3.27282637e-01
-4.28829014e-01 -5.24474025e-01 -9.52161029e-02 -4.84766245e-01
-2.25950330e-02 -1.70487642e-01 1.77773193e-01 2.71949284... | [7.319064617156982, 0.6742533445358276] |
c5b643e9-c6fc-42fc-89a8-82e8f5bf00eb | fusing-video-and-inertial-sensor-data-for | 1802.07021 | null | http://arxiv.org/abs/1802.07021v1 | http://arxiv.org/pdf/1802.07021v1.pdf | Fusing Video and Inertial Sensor Data for Walking Person Identification | An autonomous computer system (such as a robot) typically needs to identify,
locate, and track persons appearing in its sight. However, most solutions have
their limitations regarding efficiency, practicability, or environmental
constraints. In this paper, we propose an effective and practical system which
combines vid... | ['Yu-Chee Tseng', 'Yuehong Huang'] | 2018-02-20 | null | null | null | null | ['person-identification'] | ['computer-vision'] | [-1.39622107e-01 -3.96219671e-01 9.38704982e-02 -1.29278839e-01
-1.85256004e-01 -2.64047444e-01 2.87490696e-01 -1.22580804e-01
-7.24677980e-01 8.80555093e-01 1.19955927e-01 2.94077486e-01
-4.25437801e-02 -7.40646243e-01 -2.62658536e-01 -4.73074824e-01
9.89534929e-02 2.88046092e-01 4.57889825e-01 1.60327375... | [7.083743572235107, 0.2069411277770996] |
7f9f7160-3d0b-4dff-86ec-29c543330808 | cost-minimization-predictive-energy | 2212.13661 | null | https://arxiv.org/abs/2212.13661v2 | https://arxiv.org/pdf/2212.13661v2.pdf | Cost-minimization predictive energy management of a postal-delivery fuel cell electric vehicle with intelligent battery State-of-Charge Planner | Fuel cell electric vehicles have earned substantial attentions in recent decades due to their high-efficiency and zero-emission features, while the high operating costs remain the major barrier towards their large-scale commercialization. In such context, this paper aims to devise an energy management strategy for an u... | ['Ruiqing Ma', 'Marie-Cécile Péra', 'Alexandre Ravey', 'Zhen Zhang', 'Xianfeng Xu', 'Fuzeng Li', 'Yang Zhou'] | 2022-12-28 | null | null | null | null | ['energy-management'] | ['time-series'] | [-2.12769285e-02 -1.46804407e-01 -4.42522258e-01 -1.41892835e-01
-6.49699867e-01 -3.22832227e-01 5.90713263e-01 2.03866109e-01
-3.88343155e-01 9.44467545e-01 -3.88638735e-01 -3.90553296e-01
-4.55532581e-01 -8.29382241e-01 -5.42552173e-01 -1.01016557e+00
-1.44896489e-02 1.37712821e-01 5.28745120e-03 -5.52806035... | [5.612551212310791, 2.1952364444732666] |
3afa39f3-8c3f-487a-a1d3-690254328829 | assessment-of-cognitive-characteristics-in | 2209.11761 | null | https://arxiv.org/abs/2209.11761v1 | https://arxiv.org/pdf/2209.11761v1.pdf | Assessment of cognitive characteristics in intelligent systems and predictive ability | The article proposes a universal dual-axis intelligent systems assessment scale. The scale considers the properties of intelligent systems within the environmental context, which develops over time. In contrast to the frequent consideration of the 'mind' of artificial intelligent systems on a scale from 'weak' to 'stro... | ['Nadezhda G. Bagdasaryan', 'Sergey V. Kovalchuk', 'Oleg V. Kubryak'] | 2022-09-16 | null | null | null | null | ['common-sense-reasoning'] | ['reasoning'] | [ 2.23952264e-01 5.42571861e-03 1.11518716e-02 -6.62318543e-02
1.92408010e-01 -6.28630936e-01 6.78441226e-01 6.53558373e-01
-6.93342388e-01 2.31646046e-01 2.48197734e-01 -5.13782442e-01
-1.02853656e+00 -8.80384207e-01 -9.62221324e-02 -4.40847516e-01
2.55512506e-01 1.70906559e-01 2.50969112e-01 -4.94224191... | [8.963595390319824, 6.362616062164307] |
6a316c68-1f63-4a46-8be3-12114c5c1817 | pcg-based-static-underground-garage-scenario | 2307.03988 | null | https://arxiv.org/abs/2307.03988v1 | https://arxiv.org/pdf/2307.03988v1.pdf | PCG-based Static Underground Garage Scenario Generation | Autonomous driving technology has five levels, from L0 to L5. Currently, only the L2 level (partial automation) can be achieved, and there is a long way to go before reaching the final level of L5 (full automation). The key to crossing these levels lies in training the autonomous driving model. However, relying solely ... | ['Kai Li', 'Wenjin Li'] | 2023-07-08 | null | null | null | null | ['autonomous-driving'] | ['computer-vision'] | [ 2.80960321e-01 4.48288262e-01 2.11198255e-01 -5.14687777e-01
-4.56551403e-01 -4.17055577e-01 6.77824616e-01 -1.26023144e-01
-1.09372109e-01 8.31821442e-01 -2.73979753e-01 -1.04570401e+00
-1.53072253e-01 -1.42511284e+00 -7.28403270e-01 -2.61599511e-01
-5.80236688e-02 7.77026415e-01 7.44469464e-01 -7.70631313... | [8.287989616394043, -2.115488290786743] |
6de3912b-d5a7-4314-9d32-ed055d6ccaef | source-free-domain-adaptation-via | 2204.11257 | null | https://arxiv.org/abs/2204.11257v1 | https://arxiv.org/pdf/2204.11257v1.pdf | Source-Free Domain Adaptation via Distribution Estimation | Domain Adaptation aims to transfer the knowledge learned from a labeled source domain to an unlabeled target domain whose data distributions are different. However, the training data in source domain required by most of the existing methods is usually unavailable in real-world applications due to privacy preserving pol... | ['DaCheng Tao', 'Yunhe Wang', 'Chao Xu', 'Yehui Tang', 'Yixing Xu', 'Ning Ding'] | 2022-04-24 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Ding_Source-Free_Domain_Adaptation_via_Distribution_Estimation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Ding_Source-Free_Domain_Adaptation_via_Distribution_Estimation_CVPR_2022_paper.pdf | cvpr-2022-1 | ['source-free-domain-adaptation'] | ['computer-vision'] | [ 1.33032486e-01 -2.09488586e-01 -4.68726784e-01 -6.82476819e-01
-1.02144659e+00 -7.31026888e-01 4.89845127e-01 3.59318554e-02
-3.67417485e-01 1.12348843e+00 2.67002322e-02 1.97838157e-01
1.33450702e-01 -6.36594594e-01 -7.42202580e-01 -1.08053815e+00
5.67054987e-01 6.00757957e-01 6.25877678e-02 1.77050635... | [10.490482330322266, 3.1490025520324707] |
ac6637aa-fbfc-4553-931f-d94829dc392d | design-pseudo-ground-truth-with-motion-cue | 1812.05206 | null | http://arxiv.org/abs/1812.05206v1 | http://arxiv.org/pdf/1812.05206v1.pdf | Design Pseudo Ground Truth with Motion Cue for Unsupervised Video Object Segmentation | One major technique debt in video object segmentation is to label the object
masks for training instances. As a result, we propose to prepare inexpensive,
yet high quality pseudo ground truth corrected with motion cue for video object
segmentation training. Our method conducts semantic segmentation using instance
segme... | ['C. -C. Jay Kuo', 'Ming-Sui Lee', 'Yueru Chen', 'Ye Wang', 'Siyang Li', 'Qin Huang', 'Jongmoo Choi'] | 2018-12-13 | null | null | null | null | ['unsupervised-video-object-segmentation'] | ['computer-vision'] | [ 5.01335025e-01 6.19855477e-03 -7.74477363e-01 -3.87277156e-01
-6.65445030e-01 -6.62724316e-01 1.37651205e-01 -4.58111793e-01
-4.49781746e-01 6.08584821e-01 -4.89154875e-01 -4.00898047e-02
1.70031309e-01 -7.28490055e-01 -9.43880498e-01 -9.14835930e-01
2.04071194e-01 6.76773608e-01 9.44866240e-01 3.32964987... | [9.152753829956055, -0.17368514835834503] |
7873cb16-9a8c-4341-abe9-13a9528e9db4 | cascadetabnet-an-approach-for-end-to-end | 2004.12629 | null | https://arxiv.org/abs/2004.12629v2 | https://arxiv.org/pdf/2004.12629v2.pdf | CascadeTabNet: An approach for end to end table detection and structure recognition from image-based documents | An automatic table recognition method for interpretation of tabular data in document images majorly involves solving two problems of table detection and table structure recognition. The prior work involved solving both problems independently using two separate approaches. More recent works signify the use of deep learn... | ['Manish Visave', 'Kshitij Kapadni', 'Devashish Prasad', 'Ayan Gadpal', 'Kavita Sultanpure'] | 2020-04-27 | null | null | null | null | ['table-recognition', 'table-detection'] | ['computer-vision', 'miscellaneous'] | [ 9.09943972e-03 2.02529952e-01 -1.67931825e-01 -4.28389132e-01
-1.34652317e+00 -8.13085198e-01 4.11976844e-01 3.15059066e-01
-1.63897529e-01 3.58182102e-01 2.79419988e-01 -4.39033240e-01
3.00182134e-01 -8.92535865e-01 -1.10759234e+00 -1.27783924e-01
6.98731616e-02 8.77342999e-01 -1.00522913e-01 -2.73828059... | [11.69408893585205, 3.0121941566467285] |
a4117d29-ad7a-44c5-9b1c-b928aa088926 | few-shot-parameter-efficient-fine-tuning-is | 2205.05638 | null | https://arxiv.org/abs/2205.05638v2 | https://arxiv.org/pdf/2205.05638v2.pdf | Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning | Few-shot in-context learning (ICL) enables pre-trained language models to perform a previously-unseen task without any gradient-based training by feeding a small number of training examples as part of the input. ICL incurs substantial computational, memory, and storage costs because it involves processing all of the tr... | ['Colin Raffel', 'Mohit Bansal', 'Tenghao Huang', 'Jay Mohta', 'Mohammed Muqeeth', 'Derek Tam', 'Haokun Liu'] | 2022-05-11 | null | null | null | null | ['few-shot-text-classification'] | ['natural-language-processing'] | [ 2.08973870e-01 -2.45274916e-01 -1.44000202e-01 -4.21164125e-01
-7.09617615e-01 -3.12106758e-01 8.59392226e-01 1.79506809e-01
-9.03046608e-01 6.51624978e-01 -6.25754893e-02 -1.57686934e-01
1.08639456e-01 -5.59332311e-01 -8.34439993e-01 -5.37697196e-01
1.30282059e-01 5.07575095e-01 5.86532414e-01 -4.42412376... | [9.892708778381348, 3.1234006881713867] |
33295f23-812b-404b-a4f0-f0377cdfbf81 | a-sequential-concept-drift-detection-method | 2212.09637 | null | https://arxiv.org/abs/2212.09637v2 | https://arxiv.org/pdf/2212.09637v2.pdf | A Sequential Concept Drift Detection Method for On-Device Learning on Low-End Edge Devices | A practical issue of edge AI systems is that data distributions of trained dataset and deployed environment may differ due to noise and environmental changes over time. Such a phenomenon is known as a concept drift, and this gap degrades the performance of edge AI systems and may introduce system failures. To address t... | ['Hiroki Matsutani', 'Takeya Yamada'] | 2022-12-19 | null | null | null | null | ['pico'] | ['natural-language-processing'] | [ 2.10854083e-01 -2.74320126e-01 -2.45857816e-02 -1.72644593e-02
2.21962094e-01 -3.96237612e-01 4.02436741e-02 3.14298242e-01
-6.61248863e-01 1.03513551e+00 -6.80397987e-01 -1.13894112e-01
-4.48205620e-02 -7.04916120e-01 -5.88018477e-01 -8.83266449e-01
1.41995534e-01 3.81773740e-01 5.47499716e-01 1.00267209... | [7.689472198486328, 2.6403493881225586] |
7692b379-0cfb-49f6-8295-490d1aa0f474 | 190910351 | 1909.10351 | null | https://arxiv.org/abs/1909.10351v5 | https://arxiv.org/pdf/1909.10351v5.pdf | TinyBERT: Distilling BERT for Natural Language Understanding | Language model pre-training, such as BERT, has significantly improved the performances of many natural language processing tasks. However, pre-trained language models are usually computationally expensive, so it is difficult to efficiently execute them on resource-restricted devices. To accelerate inference and reduce ... | ['Xin Jiang', 'Yichun Yin', 'Qun Liu', 'Fang Wang', 'Lifeng Shang', 'Xiaoqi Jiao', 'Xiao Chen', 'Linlin Li'] | 2019-09-23 | null | https://aclanthology.org/2020.findings-emnlp.372 | https://aclanthology.org/2020.findings-emnlp.372.pdf | findings-of-the-association-for-computational | ['linguistic-acceptability'] | ['natural-language-processing'] | [-3.94861162e-01 2.96371996e-01 -4.54401881e-01 -3.68308961e-01
-8.34414780e-01 -5.99639356e-01 4.13905352e-01 8.33936632e-02
-7.92147279e-01 7.50547349e-01 -1.75287947e-01 -7.34242737e-01
1.69177562e-01 -1.12513340e+00 -1.15330768e+00 -5.24880767e-01
2.86793709e-01 9.62771893e-01 4.83911902e-01 -1.91444457... | [8.796161651611328, 3.682978868484497] |
3d6f829b-5c49-4595-b848-329cb4515686 | a-search-without-expansions-learning | 2102.04518 | null | https://arxiv.org/abs/2102.04518v2 | https://arxiv.org/pdf/2102.04518v2.pdf | A* Search Without Expansions: Learning Heuristic Functions with Deep Q-Networks | Efficiently solving problems with large action spaces using A* search has been of importance to the artificial intelligence community for decades. This is because the computation and memory requirements of A* search grow linearly with the size of the action space. This burden becomes even more apparent when A* search u... | ['Pierre Baldi', 'Roy Fox', 'Stephen Mcaleer', 'Alexander Shmakov', 'Forest Agostinelli'] | 2021-02-08 | null | null | null | null | ['rubik-s-cube'] | ['graphs'] | [ 2.71979034e-01 5.97956002e-01 -2.04744503e-01 1.45412594e-01
-6.81745529e-01 -7.15050042e-01 2.85204165e-02 2.08958685e-01
-5.85515380e-01 1.02394712e+00 -1.24336220e-01 -5.52938879e-01
-5.63205004e-01 -1.38233387e+00 -9.52971041e-01 -6.25715792e-01
-4.58117098e-01 7.68628418e-01 1.02305217e-02 -3.20474684... | [5.147482872009277, 2.9710733890533447] |
b0247371-7489-4b1e-9e36-fdd89d94e9f9 | automated-evaluation-of-scientific-writing | null | null | https://aclanthology.org/W15-0607 | https://aclanthology.org/W15-0607.pdf | Automated Evaluation of Scientific Writing: AESW Shared Task Proposal | null | ['Vidas Daudaravi{\\v{c}}ius'] | 2015-06-01 | null | null | null | ws-2015-6 | ['grammatical-error-detection'] | ['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... | [-7.175906181335449, 3.5512032508850098] |
9dd21300-270f-46e7-bc9d-033d38741c6a | detection-of-inferior-myocardial-infarction | 1710.01115 | null | http://arxiv.org/abs/1710.01115v4 | http://arxiv.org/pdf/1710.01115v4.pdf | Detection of Inferior Myocardial Infarction using Shallow Convolutional Neural Networks | Myocardial Infarction is one of the leading causes of death worldwide. This
paper presents a Convolutional Neural Network (CNN) architecture which takes
raw Electrocardiography (ECG) signal from lead II, III and AVF and
differentiates between inferior myocardial infarction (IMI) and healthy
signals. The performance of ... | ['Tahsin Reasat', 'Celia Shahnaz'] | 2017-10-03 | null | null | null | null | ['electrocardiography-ecg'] | ['methodology'] | [ 2.29093477e-01 -5.67900538e-02 2.21512362e-01 -1.53920963e-01
-3.88514578e-01 -3.40330154e-01 8.74138549e-02 3.93815368e-01
-7.15982497e-01 7.28937328e-01 -1.05203986e-01 -4.62318718e-01
-5.10690451e-01 -6.04976594e-01 -1.68493614e-01 -6.62189841e-01
-7.55776525e-01 2.42816731e-01 -3.30124438e-01 5.23651913... | [14.32568645477295, 3.2830898761749268] |
0c35e7bf-664e-406f-87ee-2eac9e48055c | efficient-partial-credit-grading-of-proof | 2204.04196 | null | https://arxiv.org/abs/2204.04196v3 | https://arxiv.org/pdf/2204.04196v3.pdf | Efficient Feedback and Partial Credit Grading for Proof Blocks Problems | Proof Blocks is a software tool that allows students to practice writing mathematical proofs by dragging and dropping lines instead of writing proofs from scratch. Proof Blocks offers the capability of assigning partial credit and providing solution quality feedback to students. This is done by computing the edit dista... | ['Matthew West', 'Geoffrey Herman', 'Shubhang Kulkarni', 'Seth Poulsen'] | 2022-04-08 | null | null | null | null | ['mathematical-proofs'] | ['miscellaneous'] | [ 2.12653711e-01 1.45273358e-01 -9.65353176e-02 -3.12042654e-01
-7.13779449e-01 -1.14231789e+00 -1.91356510e-01 8.57278347e-01
-1.50781378e-01 7.61683285e-01 -6.93639278e-01 -1.12609446e+00
-4.86614108e-01 -1.04319739e+00 -1.06345153e+00 -1.63884595e-01
-1.99042618e-01 3.57199013e-01 5.76602876e-01 -2.33553052... | [9.680899620056152, 7.324916362762451] |
11fc6db3-68fd-458d-902f-b0d197c45ded | a-recipe-for-efficient-sbir-models-combining | 2305.18988 | null | https://arxiv.org/abs/2305.18988v1 | https://arxiv.org/pdf/2305.18988v1.pdf | A Recipe for Efficient SBIR Models: Combining Relative Triplet Loss with Batch Normalization and Knowledge Distillation | Sketch-Based Image Retrieval (SBIR) is a crucial task in multimedia retrieval, where the goal is to retrieve a set of images that match a given sketch query. Researchers have already proposed several well-performing solutions for this task, but most focus on enhancing embedding through different approaches such as trip... | ['Thierry Dutoit', 'Stéphane Dupont', 'Nathan Hubens', 'Omar Seddati'] | 2023-05-30 | null | null | null | null | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 2.02255011e-01 -2.44670421e-01 -3.18722874e-01 -1.56839982e-01
-1.11337924e+00 -6.32340372e-01 9.21093643e-01 1.16568498e-01
-6.29469633e-01 3.96072447e-01 1.78942665e-01 -5.00364192e-02
-2.82795608e-01 -7.04796433e-01 -8.38355184e-01 -4.83978570e-01
-5.91553710e-02 2.20899194e-01 2.72342205e-01 -2.74314880... | [11.413434982299805, 0.6259356141090393] |
af599f7c-d442-422f-b8d2-6e13de7d185f | geometric-feature-based-facial-expression | 1604.03225 | null | http://arxiv.org/abs/1604.03225v1 | http://arxiv.org/pdf/1604.03225v1.pdf | Geometric Feature-Based Facial Expression Recognition in Image Sequences Using Multi-Class AdaBoost and Support Vector Machines | Facial expressions are widely used in the behavioral interpretation of
emotions, cognitive science, and social interactions. In this paper, we present
a novel method for fully automatic facial expression recognition in facial
image sequences. As the facial expression evolves over time facial landmarks
are automatically... | ['Joonwhoan Lee', 'Deepak Ghimire'] | 2016-04-12 | null | null | null | null | ['landmark-tracking'] | ['computer-vision'] | [-1.26297716e-02 -5.15876651e-01 -2.86835313e-01 -7.05294907e-01
-4.66477394e-01 -3.53444785e-01 4.26487803e-01 -2.06007466e-01
-7.11297333e-01 4.18582380e-01 8.22572485e-02 6.79253101e-01
-2.85624359e-02 -3.40742886e-01 -1.66353971e-01 -1.30634785e+00
-3.75496894e-01 1.20388001e-01 -1.85300454e-01 -3.79873484... | [13.638944625854492, 1.8270667791366577] |
aab28b4f-5b37-4fc5-933f-fc42d4d45dc0 | bayesimp-uncertainty-quantification-for | 2106.03477 | null | https://arxiv.org/abs/2106.03477v1 | https://arxiv.org/pdf/2106.03477v1.pdf | BayesIMP: Uncertainty Quantification for Causal Data Fusion | While causal models are becoming one of the mainstays of machine learning, the problem of uncertainty quantification in causal inference remains challenging. In this paper, we study the causal data fusion problem, where datasets pertaining to multiple causal graphs are combined to estimate the average treatment effect ... | ['Dino Sejdinovic', 'Yee Whye Teh', 'Javier González', 'Jean-François Ton', 'Siu Lun Chau'] | 2021-06-07 | null | http://proceedings.neurips.cc/paper/2021/hash/1ca5c750a30312d1919ae6a4d636dcc4-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/1ca5c750a30312d1919ae6a4d636dcc4-Paper.pdf | neurips-2021-12 | ['bayesian-optimisation'] | ['methodology'] | [ 4.11309630e-01 3.13403249e-01 -1.47643968e-01 -2.25298777e-01
-8.45178127e-01 -5.74220896e-01 9.06297505e-01 6.58676922e-01
-7.67921060e-02 8.40656698e-01 7.97300339e-01 -2.20539570e-01
-9.50088620e-01 -7.75584638e-01 -8.24561954e-01 -7.78957069e-01
-2.43384928e-01 3.75833035e-01 -3.37860435e-01 4.07773942... | [7.849324703216553, 5.34016752243042] |
c81b29b3-f0a5-43ae-834f-f4987db87c68 | findings-of-the-2018-conference-on-machine | null | null | https://aclanthology.org/W18-6401 | https://aclanthology.org/W18-6401.pdf | Findings of the 2018 Conference on Machine Translation (WMT18) | This paper presents the results of the premier shared task organized alongside the Conference on Machine Translation (WMT) 2018. Participants were asked to build machine translation systems for any of 7 language pairs in both directions, to be evaluated on a test set of news stories. The main metric for this task is hu... | ['Ond{\\v{r}}ej Bojar', 'Christof Monz', 'Barry Haddow', 'Mark Fishel', 'Yvette Graham', 'Philipp Koehn', 'Christian Federmann'] | 2018-10-01 | null | null | null | ws-2018-10 | ['multimodal-machine-translation'] | ['natural-language-processing'] | [ 3.35952312e-01 7.37498477e-02 -4.73571032e-01 -5.85181892e-01
-1.69938660e+00 -9.39016879e-01 1.15444458e+00 -9.68473032e-02
-4.94481534e-01 1.09894836e+00 4.47093964e-01 -7.11024046e-01
4.14420962e-01 -2.67592698e-01 -8.23859572e-01 1.96255639e-01
3.92123669e-01 1.06781638e+00 -2.52873451e-01 -7.71999359... | [11.544992446899414, 10.291520118713379] |
e4d9f44b-060e-4314-8c75-097fe839811b | timing-process-interventions-with-causal | 2306.04299 | null | https://arxiv.org/abs/2306.04299v1 | https://arxiv.org/pdf/2306.04299v1.pdf | Timing Process Interventions with Causal Inference and Reinforcement Learning | The shift from the understanding and prediction of processes to their optimization offers great benefits to businesses and other organizations. Precisely timed process interventions are the cornerstones of effective optimization. Prescriptive process monitoring (PresPM) is the sub-field of process mining that concentra... | ['Jochen De Weerdt', 'Wouter Verbeke', 'Hans Weytjens'] | 2023-06-07 | null | null | null | null | ['causal-inference', 'causal-inference'] | ['knowledge-base', 'miscellaneous'] | [ 2.77513444e-01 2.69899756e-01 -6.38742566e-01 1.49042040e-01
-2.60371238e-01 -3.25747877e-01 1.00661945e+00 7.08312035e-01
-3.45243126e-01 6.53025925e-01 1.59501404e-01 -5.91189981e-01
-8.66225600e-01 -8.05424035e-01 -5.36598742e-01 -4.91578639e-01
-4.39173013e-01 7.89476454e-01 1.54625311e-01 6.51329160... | [8.58629322052002, 5.956803321838379] |
45c29ed0-b4dd-49f8-9f00-7194a2cbb4f9 | combining-global-and-local-attention-with | null | null | https://www.iti.gr/~bmezaris/publications/ism2021a_preprint.pdf | https://www.iti.gr/~bmezaris/publications/ism2021a_preprint.pdf | Combining Global and Local Attention with Positional Encoding for Video Summarization | This paper presents a new method for supervised video summarization. To overcome drawbacks of existing RNN-based summarization architectures, that relate to the modeling of long-range frames' dependencies and the ability to parallelize the training process, the developed model relies on the use of self-attention mechan... | ['Ioannis Patras', 'Vasileios Mezaris', 'Georgios Balaouras', 'Evlampios Apostolidis'] | 2021-12-01 | null | null | null | ieee-international-symposium-on-multimedia-1 | ['supervised-video-summarization'] | ['computer-vision'] | [ 2.79014230e-01 3.94492656e-01 -2.54655689e-01 -1.22021019e-01
-8.71765733e-01 -1.39607102e-01 7.91803181e-01 3.88913780e-01
-5.86975873e-01 8.03394914e-01 9.54939485e-01 1.00226864e-01
-1.50716707e-01 -4.08546358e-01 -6.86322212e-01 -5.12989342e-01
4.18317728e-02 3.57305557e-01 4.60248739e-01 -3.03092659... | [10.418336868286133, 0.43520259857177734] |
27b2b8f1-1991-4c15-a99d-3a017f355066 | learning-latent-semantic-annotations-for | null | null | https://aclanthology.org/D18-1411 | https://aclanthology.org/D18-1411.pdf | Learning Latent Semantic Annotations for Grounding Natural Language to Structured Data | Previous work on grounded language learning did not fully capture the semantics underlying the correspondences between structured world state representations and texts, especially those between numerical values and lexical terms. In this paper, we attempt at learning explicit latent semantic annotations from paired str... | ['Chin-Yew Lin', 'Jin-Ge Yao', 'Guanghui Qin', 'Jinpeng Wang', 'Xuening Wang'] | 2018-10-01 | null | null | null | emnlp-2018-10 | ['grounded-language-learning'] | ['natural-language-processing'] | [ 2.14402750e-01 3.92959297e-01 -6.39584959e-01 -4.37185705e-01
-7.83315063e-01 -6.19703829e-01 1.02739680e+00 3.76782805e-01
-3.12104434e-01 8.92022371e-01 1.00777614e+00 -1.91755041e-01
1.35416672e-01 -9.24503922e-01 -8.02460611e-01 -3.03664088e-01
1.29791856e-01 3.88384432e-01 1.47437915e-01 -1.17790371... | [10.587059020996094, 8.761520385742188] |
051c339c-619c-4cee-8602-77b93a160822 | towards-learning-representations-of-binary | 2002.03388 | null | https://arxiv.org/abs/2002.03388v2 | https://arxiv.org/pdf/2002.03388v2.pdf | Bin2vec: Learning Representations of Binary Executable Programs for Security Tasks | Tackling binary program analysis problems has traditionally implied manually defining rules and heuristics, a tedious and time-consuming task for human analysts. In order to improve automation and scalability, we propose an alternative direction based on distributed representations of binary programs with applicability... | ['Erik Kline', 'Sima Arasteh', 'Shushan Arakelyan', 'Aram Galstyan', 'Christophe Hauser'] | 2020-02-09 | null | null | null | null | ['vulnerability-detection'] | ['miscellaneous'] | [-7.19582438e-02 -1.17952816e-01 -4.88915533e-01 -3.79529536e-01
-6.49030507e-01 -9.05099213e-01 3.07369798e-01 6.77370071e-01
-2.16062427e-01 2.24102020e-01 1.14854440e-01 -9.35606956e-01
8.63250867e-02 -1.11490011e+00 -7.26723969e-01 -1.69378296e-01
-2.07688034e-01 4.63285178e-01 3.44410926e-01 -4.11378145... | [7.185121059417725, 7.777173042297363] |
d117f151-24dd-4b36-87d8-01640c853ec5 | semantic-metadata-extraction-from-dense-video | 2211.02982 | null | https://arxiv.org/abs/2211.02982v1 | https://arxiv.org/pdf/2211.02982v1.pdf | Semantic Metadata Extraction from Dense Video Captioning | Annotation of multimedia data by humans is time-consuming and costly, while reliable automatic generation of semantic metadata is a major challenge. We propose a framework to extract semantic metadata from automatically generated video captions. As metadata, we consider entities, the entities' properties, relations bet... | ['Deepayan Bhowmik', 'Ansgar Scherp', 'Johannes Scherer'] | 2022-11-05 | null | null | null | null | ['dense-video-captioning'] | ['computer-vision'] | [ 2.03926265e-01 3.64661485e-01 2.79197432e-02 -3.03726673e-01
-1.06023967e+00 -7.85309196e-01 8.55747700e-01 4.22717035e-01
-3.92846256e-01 8.16054404e-01 7.19180524e-01 2.56981939e-01
3.92853796e-01 -5.58156252e-01 -1.24918342e+00 -2.22003162e-01
-7.38517940e-02 5.26572168e-01 6.39067352e-01 1.42965406... | [10.50901985168457, 0.6712035536766052] |
076ba50e-c52a-4e19-872e-d8ac460f618a | unsupervised-classification-for-polarimetric | 2104.01656 | null | https://arxiv.org/abs/2104.01656v1 | https://arxiv.org/pdf/2104.01656v1.pdf | Unsupervised Classification for Polarimetric SAR Data Using Variational Bayesian Wishart Mixture Model with Inverse Gamma-Gamma Prior | Although various clustering methods have been successfully applied to polarimetric synthetic aperture radar (PolSAR) image clustering tasks, most of the available approaches fail to realize automatic determination of cluster number, nor have they derived an exact distribution for the number of looks. To overcome these ... | ['Changlong Wang', 'Feng Zhou', 'Shijie Ren'] | 2021-04-04 | null | null | null | null | ['image-clustering'] | ['computer-vision'] | [-7.49910176e-02 -3.91057849e-01 3.18318307e-01 -6.11658454e-01
-9.57028985e-01 -4.13472086e-01 6.36717319e-01 -1.89380705e-01
-3.04047078e-01 6.17210031e-01 -4.32658801e-03 -2.31462628e-01
-7.09516048e-01 -4.57289308e-01 -4.68205698e-02 -1.24474382e+00
7.58570246e-03 6.27599895e-01 8.65852535e-02 1.09741427... | [10.022819519042969, -2.0491387844085693] |
064ebbd2-42b9-4af1-bc47-b6f494d33316 | hector-a-hybrid-text-simplification-tool-for | null | null | https://aclanthology.org/2022.lrec-1.493 | https://aclanthology.org/2022.lrec-1.493.pdf | HECTOR: A Hybrid TExt SimplifiCation TOol for Raw Texts in French | Reducing the complexity of texts by applying an Automatic Text Simplification (ATS) system has been sparking interest inthe area of Natural Language Processing (NLP) for several years and a number of methods and evaluation campaigns haveemerged targeting lexical and syntactic transformations. In recent years, several s... | ['Núria Gala', 'Delphine Bernhard', 'Thomas François', 'Eva Rolin', 'Rodrigo Wilkens', 'Amalia Todirascu'] | null | null | null | null | lrec-2022-6 | ['lexical-simplification'] | ['natural-language-processing'] | [ 3.06579582e-02 4.45976645e-01 -1.80485234e-01 -2.87393332e-01
-6.50688469e-01 -4.79732990e-01 1.13708687e+00 8.15926790e-01
-8.96652281e-01 7.50106037e-01 7.78469563e-01 -3.98529589e-01
-8.10760409e-02 -7.85811961e-01 -2.42696375e-01 -2.68429846e-01
3.28047484e-01 7.42895067e-01 1.99214071e-01 -6.44214809... | [10.9152193069458, 10.403144836425781] |
19404aaa-7ce0-40c7-8716-93cddada7ff5 | drs-parsing-as-sequence-labeling | null | null | https://aclanthology.org/2022.starsem-1.19 | https://aclanthology.org/2022.starsem-1.19.pdf | DRS Parsing as Sequence Labeling | We present the first fully trainable semantic parser for English, German, Italian, and Dutch discourse representation structures (DRSs) that is competitive in accuracy with recent sequence-to-sequence models and at the same time {emph{compositional} in the sense that the output maps each token to one of a finite set of... | ['Kilian Evang', 'Minxing Shen'] | null | null | null | null | sem-naacl-2022-7 | ['drs-parsing'] | ['natural-language-processing'] | [ 6.51398778e-01 7.82113194e-01 -2.35838830e-01 -7.47865617e-01
-8.28059375e-01 -1.13426328e+00 4.98660147e-01 3.73340577e-01
-3.17684978e-01 8.86332273e-01 6.80956542e-01 -6.55001163e-01
1.20528139e-01 -7.00860918e-01 -5.31919777e-01 -3.38625640e-01
1.56137154e-01 7.07792580e-01 4.29324627e-01 -3.88506353... | [10.392426490783691, 9.251577377319336] |
6957cefd-bc10-4bf1-ba46-92858d9f190c | tan-without-a-burn-scaling-laws-of-dp-sgd | 2210.03403 | null | https://arxiv.org/abs/2210.03403v2 | https://arxiv.org/pdf/2210.03403v2.pdf | TAN Without a Burn: Scaling Laws of DP-SGD | Differentially Private methods for training Deep Neural Networks (DNNs) have progressed recently, in particular with the use of massive batches and aggregated data augmentations for a large number of training steps. These techniques require much more computing resources than their non-private counterparts, shifting the... | ['Alexandre Sablayrolles', 'Pierre Stock', 'Tom Sander'] | 2022-10-07 | null | null | null | null | ['image-classification-with-dp'] | ['computer-vision'] | [ 1.28372863e-01 2.06822619e-01 2.54661918e-01 -7.82666981e-01
-9.20395076e-01 -6.71886444e-01 1.78162903e-01 -2.95989923e-02
-1.12878311e+00 8.77518654e-01 -2.75740862e-01 -5.59462547e-01
-4.50797789e-02 -5.75209856e-01 -8.64881992e-01 -1.15181029e+00
-1.62397802e-01 4.95088398e-02 -1.48882851e-01 1.69470787... | [5.9068121910095215, 6.8532490730285645] |
5e3fa616-acbd-41c5-b084-89623519380f | boosted-efficientnet-detection-of-lymph-node | 2010.05027 | null | https://arxiv.org/abs/2010.05027v1 | https://arxiv.org/pdf/2010.05027v1.pdf | Boosted EfficientNet: Detection of Lymph Node Metastases in Breast Cancer Using Convolutional Neural Network | In recent years, advances in the development of whole-slide images have laid a foundation for the utilization of digital images in pathology. With the assistance of computer images analysis that automatically identifies tissue or cell types, they have greatly improved the histopathologic interpretation and diagnosis ac... | ['Hefeng Zhou', 'Zhaogang Yang', 'Haotian Xie', 'Qianying Liu', 'Jun Wang'] | 2020-10-10 | null | null | null | null | ['image-cropping'] | ['computer-vision'] | [ 3.48002732e-01 1.75000384e-01 -2.43210301e-01 -2.79813915e-01
-6.61622405e-01 -8.03287923e-02 3.85726422e-01 1.97579145e-01
-6.85805500e-01 5.31332195e-01 1.82058208e-03 -3.16346616e-01
6.89193085e-02 -9.63183880e-01 -2.84059227e-01 -1.03713858e+00
3.54982108e-01 -1.88873291e-01 1.73910975e-01 -1.88425049... | [15.032027244567871, -2.8839986324310303] |
22a3cc74-1ffe-48c1-88c8-a1509e21f69b | segmented-learning-for-class-of-service | 2208.01793 | null | https://arxiv.org/abs/2208.01793v1 | https://arxiv.org/pdf/2208.01793v1.pdf | Segmented Learning for Class-of-Service Network Traffic Classification | Class-of-service (CoS) network traffic classification (NTC) classifies a group of similar traffic applications. The CoS classification is advantageous in resource scheduling for Internet service providers and avoids the necessity of remodelling. Our goal is to find a robust, lightweight, and fast-converging CoS classif... | ['Xiao-Ping Zhang', 'Ramy Atawia', 'Akram Bin Sediq', 'Hatem Abou-zeid', 'Sihao Zhao', 'Yoga Suhas Kuruba Manjunath'] | 2022-08-03 | null | null | null | null | ['traffic-classification'] | ['miscellaneous'] | [ 1.67237237e-01 -2.88370430e-01 -7.19782770e-01 -5.36950231e-01
-3.39518249e-01 -4.63478416e-01 2.00474948e-01 -1.08673111e-01
-8.88891146e-02 7.54645407e-01 -6.04177415e-01 -1.03353143e+00
-4.45309043e-01 -6.95040286e-01 -1.88015655e-01 -3.38251412e-01
-3.17776352e-01 6.89415872e-01 6.89890265e-01 -1.80047333... | [5.067710876464844, 7.195638656616211] |
b65e6606-cc55-4a86-a4d4-f926caddaadc | think-twice-a-human-like-two-stage | 2301.04907 | null | https://arxiv.org/abs/2301.04907v2 | https://arxiv.org/pdf/2301.04907v2.pdf | Think Twice: A Human-like Two-stage Conversational Agent for Emotional Response Generation | Towards human-like dialogue systems, current emotional dialogue approaches jointly model emotion and semantics with a unified neural network. This strategy tends to generate safe responses due to the mutual restriction between emotion and semantics, and requires rare emotion-annotated large-scale dialogue corpus. Inspi... | ['Yuexian Hou', 'Kun Huang', 'Dongming Zhao', 'Shuo Zhang', 'Wu Bin', 'Shangzhao Ma', 'Bo wang', 'Yushan Qian'] | 2023-01-12 | null | null | null | null | ['response-generation'] | ['natural-language-processing'] | [-1.88669905e-01 9.58989084e-01 1.72672838e-01 -6.75198615e-01
-2.47478724e-01 -2.90826172e-01 7.56711066e-01 -1.69600174e-01
-2.87072599e-01 9.92248058e-01 8.63208234e-01 3.66094053e-01
6.29712164e-01 -7.31896043e-01 1.97520390e-01 -3.53714049e-01
3.65029603e-01 7.81719744e-01 -3.61615747e-01 -9.45115626... | [13.104368209838867, 7.6696672439575195] |
bc44ecd0-9fde-46ff-a2d1-f4b8f3386cc3 | knowledge-graph-question-answering-datasets | 2205.06573 | null | https://arxiv.org/abs/2205.06573v1 | https://arxiv.org/pdf/2205.06573v1.pdf | Knowledge Graph Question Answering Datasets and Their Generalizability: Are They Enough for Future Research? | Existing approaches on Question Answering over Knowledge Graphs (KGQA) have weak generalizability. That is often due to the standard i.i.d. assumption on the underlying dataset. Recently, three levels of generalization for KGQA were defined, namely i.i.d., compositional, zero-shot. We analyze 25 well-known KGQA dataset... | ['Ricardo Usbeck', 'Longquan Jiang'] | 2022-05-13 | null | null | null | null | ['graph-question-answering'] | ['graphs'] | [-3.07859004e-01 4.33375180e-01 -1.42986804e-01 -4.27985817e-01
-8.23810220e-01 -9.13752615e-01 2.87100852e-01 3.68470848e-01
-8.44939873e-02 8.76328886e-01 1.12455972e-02 -4.14488137e-01
-5.48729122e-01 -1.30666411e+00 -7.71633744e-01 -1.92871168e-01
2.82021910e-01 8.88961077e-01 4.97410566e-01 -5.63078642... | [10.101189613342285, 8.019866943359375] |
d3109c66-3be3-4e2e-acdd-91393fc84f23 | chemcrow-augmenting-large-language-models | 2304.05376 | null | https://arxiv.org/abs/2304.05376v4 | https://arxiv.org/pdf/2304.05376v4.pdf | ChemCrow: Augmenting large-language models with chemistry tools | Over the last decades, excellent computational chemistry tools have been developed. Their full potential has not yet been reached as most are challenging to learn and exist in isolation. Recently, large-language models (LLMs) have shown strong performance in tasks across domains, but struggle with chemistry-related pro... | ['Philippe Schwaller', 'Andrew D White', 'Sam Cox', 'Andres M Bran'] | 2023-04-11 | null | null | null | null | ['drug-discovery'] | ['medical'] | [-2.30804831e-02 2.69930456e-02 -2.42571145e-01 -9.80472891e-04
-1.20480895e+00 -1.18728375e+00 5.82324505e-01 6.77208662e-01
-2.92779237e-01 1.01542258e+00 -6.08155578e-02 -1.00892377e+00
7.17621371e-02 -4.99929547e-01 -6.91778600e-01 -5.71950972e-01
1.85182378e-01 4.95169520e-01 1.30870892e-02 -7.11583868... | [4.847652912139893, 5.815973281860352] |
ce36dde6-1111-4e9e-b4f3-2429bd069bb8 | attentive-continuous-generative-self-training | 2305.14589 | null | https://arxiv.org/abs/2305.14589v1 | https://arxiv.org/pdf/2305.14589v1.pdf | Attentive Continuous Generative Self-training for Unsupervised Domain Adaptive Medical Image Translation | Self-training is an important class of unsupervised domain adaptation (UDA) approaches that are used to mitigate the problem of domain shift, when applying knowledge learned from a labeled source domain to unlabeled and heterogeneous target domains. While self-training-based UDA has shown considerable promise on discri... | ['Jonghye Woo', 'Georges El Fakhri', 'Maureen Stone', 'Reese Timothy', 'Jiachen Zhuo', 'Fangxu Xing', 'Jerry L. Prince', 'Xiaofeng Liu'] | 2023-05-23 | null | null | null | null | ['value-prediction', 'unsupervised-domain-adaptation', 'pseudo-label'] | ['computer-code', 'methodology', 'miscellaneous'] | [ 8.28465164e-01 5.93568802e-01 -3.75963479e-01 -7.44961023e-01
-1.40636778e+00 -4.36484933e-01 6.11219943e-01 -3.28407943e-01
-2.58223683e-01 1.03383982e+00 2.03649089e-01 -1.96192399e-01
-1.00210207e-02 -5.57292759e-01 -9.97769892e-01 -9.32671010e-01
4.39760625e-01 8.06082785e-01 6.37218636e-03 7.70924240... | [14.573246002197266, -1.9723126888275146] |
098d2457-7880-432c-8572-6e3c6aff5633 | onsets-and-frames-dual-objective-piano | 1710.11153 | null | http://arxiv.org/abs/1710.11153v2 | http://arxiv.org/pdf/1710.11153v2.pdf | Onsets and Frames: Dual-Objective Piano Transcription | We advance the state of the art in polyphonic piano music transcription by
using a deep convolutional and recurrent neural network which is trained to
jointly predict onsets and frames. Our model predicts pitch onset events and
then uses those predictions to condition framewise pitch predictions. During
inference, we r... | ['Jesse Engel', 'Adam Roberts', 'Douglas Eck', 'Sageev Oore', 'Jialin Song', 'Ian Simon', 'Erich Elsen', 'Curtis Hawthorne', 'Colin Raffel'] | 2017-10-30 | null | null | null | null | ['music-transcription'] | ['music'] | [ 4.69559819e-01 -2.93350574e-02 -1.37735307e-02 -1.68791618e-02
-8.07084680e-01 -7.77003944e-01 3.03433836e-01 7.64264166e-02
-2.11037099e-01 4.49788004e-01 7.76829898e-01 1.43119037e-01
7.08223283e-02 -4.44865853e-01 -6.51676297e-01 -5.77320695e-01
-1.83075413e-01 1.98799111e-02 1.17469475e-01 -2.03514785... | [15.849663734436035, 5.427392482757568] |
ace753cd-b5d2-465c-89ee-cf920109640b | sphere-embedding-an-application-to-part-of | null | null | http://papers.nips.cc/paper/3979-sphere-embedding-an-application-to-part-of-speech-induction | http://papers.nips.cc/paper/3979-sphere-embedding-an-application-to-part-of-speech-induction.pdf | Sphere Embedding: An Application to Part-of-Speech Induction | Motivated by an application to unsupervised part-of-speech tagging, we present an algorithm for the Euclidean embedding of large sets of categorical data based on co-occurrence statistics. We use the CODE model of Globerson et al. but constrain the embedding to lie on a high-dimensional unit sphere. This constraint all... | ['Yariv Maron', 'Michael Lamar', 'Elie Bienenstock'] | 2010-12-01 | null | null | null | neurips-2010-12 | ['unsupervised-part-of-speech-tagging'] | ['natural-language-processing'] | [ 3.01183164e-02 5.09605050e-01 -6.34027198e-02 -2.10130006e-01
-7.50174463e-01 -7.62923539e-01 5.74016273e-01 3.95262182e-01
-6.90833032e-01 3.12996149e-01 6.17031991e-01 -5.34209430e-01
-2.36417770e-01 -6.39627814e-01 -4.00300086e-01 -7.21899927e-01
-4.97673273e-01 5.07542431e-01 2.88612306e-01 2.97259204... | [10.31196403503418, 8.404583930969238] |
1fc38147-6c16-4752-8005-86069ea83626 | generalized-transition-based-dependency | null | null | https://aclanthology.org/P16-1015 | https://aclanthology.org/P16-1015.pdf | Generalized Transition-based Dependency Parsing via Control Parameters | null | ['Emily Pitler', 'Ryan Mcdonald', 'Bernd Bohnet', 'Ji Ma'] | 2016-08-01 | null | null | null | acl-2016-8 | ['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... | [-7.282987117767334, 3.749140977859497] |
f9ebba1f-e0b4-48a1-8769-22c5af17f6f1 | model-extraction-attacks-on-split-federated | 2303.08581 | null | https://arxiv.org/abs/2303.08581v1 | https://arxiv.org/pdf/2303.08581v1.pdf | Model Extraction Attacks on Split Federated Learning | Federated Learning (FL) is a popular collaborative learning scheme involving multiple clients and a server. FL focuses on protecting clients' data but turns out to be highly vulnerable to Intellectual Property (IP) threats. Since FL periodically collects and distributes the model parameters, a free-rider can download t... | ['Chaitali Chakrabarti', 'Deliang Fan', 'Zhezhi He', 'Li Yang', 'Xing Chen', 'Adnan Siraj Rakin', 'Jingtao Li'] | 2023-03-13 | null | null | null | null | ['blocking'] | ['natural-language-processing'] | [-1.41553521e-01 -5.90795092e-02 -3.65490407e-01 -1.64579481e-01
-8.46108556e-01 -1.06827784e+00 2.47602656e-01 -2.27244318e-01
-3.19220781e-01 7.30714560e-01 -2.64060766e-01 -9.23489809e-01
-1.13064125e-01 -9.18586135e-01 -8.47305298e-01 -7.89331257e-01
-2.54088104e-01 2.01547489e-01 7.26854622e-01 9.80523378... | [5.794227123260498, 6.840498924255371] |
13508250-8a43-41ca-ab3c-9e818e4f2b37 | clear-a-dataset-for-compositional-language | 1811.10561 | null | http://arxiv.org/abs/1811.10561v1 | http://arxiv.org/pdf/1811.10561v1.pdf | CLEAR: A Dataset for Compositional Language and Elementary Acoustic Reasoning | We introduce the task of acoustic question answering (AQA) in the area of
acoustic reasoning. In this task an agent learns to answer questions on the
basis of acoustic context. In order to promote research in this area, we
propose a data generation paradigm adapted from CLEVR (Johnson et al. 2017). We
generate acoustic... | ['Jerome Abdelnour', 'Jean Rouat', 'Giampiero Salvi'] | 2018-11-26 | null | null | null | null | ['acoustic-question-answering'] | ['speech'] | [ 4.31738555e-01 1.97258398e-01 6.85514212e-01 -4.79682893e-01
-1.09953082e+00 -6.68971598e-01 7.40524590e-01 1.51965499e-01
-3.21242332e-01 6.04372248e-02 4.49731827e-01 -4.37690794e-01
-1.12060905e-01 -1.02585447e+00 -8.07638347e-01 -2.19163522e-01
6.45997524e-02 3.57881397e-01 4.36778009e-01 -4.68900770... | [15.143771171569824, 5.101315975189209] |
f460cc5a-7605-4625-8d7d-f88f691a0539 | identifying-nonlinear-dynamical-systems-from | 2111.02922 | null | https://arxiv.org/abs/2111.02922v3 | https://arxiv.org/pdf/2111.02922v3.pdf | Reconstructing Nonlinear Dynamical Systems from Multi-Modal Time Series | Empirically observed time series in physics, biology, or medicine, are commonly generated by some underlying dynamical system (DS) which is the target of scientific interest. There is an increasing interest to harvest machine learning methods to reconstruct this latent DS in a data-driven, unsupervised way. In many are... | ['Daniel Kramer', 'Daniel Durstewitz', 'Georgia Koppe', 'Carlo Tombolini', 'Philine Lou Bommer'] | 2021-11-04 | null | null | null | null | ['data-integration'] | ['knowledge-base'] | [ 4.76246864e-01 -4.39086668e-02 -7.02930912e-02 -1.96971223e-01
-7.85621762e-01 -6.45348072e-01 7.39014030e-01 -1.11741468e-01
-3.11903894e-01 6.99270606e-01 2.54888773e-01 -1.74288198e-01
-3.97199124e-01 -1.84445053e-01 -9.84218597e-01 -1.11998367e+00
2.56277502e-01 5.89230895e-01 -2.38579009e-02 -2.19097584... | [6.713119983673096, 3.543926477432251] |
49b15fdd-3806-488d-8b0b-20e6a1cb9f80 | referring-expressions-with-rational-speech | 2205.07795 | null | https://arxiv.org/abs/2205.07795v1 | https://arxiv.org/pdf/2205.07795v1.pdf | Referring Expressions with Rational Speech Act Framework: A Probabilistic Approach | This paper focuses on a referring expression generation (REG) task in which the aim is to pick out an object in a complex visual scene. One common theoretical approach to this problem is to model the task as a two-agent cooperative scheme in which a `speaker' agent would generate the expression that best describes a ta... | ['Sang Chin', 'Elizabeth Coppock', 'Derry Wijaya', 'Fabian Zhafransyah', 'Taufiq Daryanto', 'Hieu Le'] | 2022-05-16 | null | null | null | null | ['referring-expression-generation'] | ['computer-vision'] | [ 2.73661584e-01 7.51440823e-01 1.23209447e-01 -7.45112658e-01
-1.26760876e+00 -3.53639513e-01 8.57839525e-01 -1.62933230e-01
-2.80643761e-01 7.63126075e-01 6.34473026e-01 -1.19035408e-01
-1.21299192e-01 -5.01008928e-01 -6.39946699e-01 -8.17107737e-01
1.16229683e-01 9.20911670e-01 -1.93083972e-01 -3.39343697... | [10.789072036743164, 1.6665418148040771] |
7dad1ae1-d6e9-476b-a8a0-e31e608dd7a3 | offline-reinforcement-learning-with-6 | 2305.12679 | null | https://arxiv.org/abs/2305.12679v2 | https://arxiv.org/pdf/2305.12679v2.pdf | Offline Reinforcement Learning with Additional Covering Distributions | We study learning optimal policies from a logged dataset, i.e., offline RL, with function approximation. Despite the efforts devoted, existing algorithms with theoretic finite-sample guarantees typically assume exploratory data coverage or strong realizable function classes, which is hard to be satisfied in reality. Wh... | ['Chenjie Mao'] | 2023-05-22 | null | null | null | null | ['offline-rl'] | ['playing-games'] | [ 1.36485800e-01 7.32216835e-01 -8.06127965e-01 -4.11503250e-04
-1.17791140e+00 -9.22696054e-01 1.99493334e-01 2.21380249e-01
-3.06598127e-01 1.30055678e+00 6.24695458e-02 -4.83512700e-01
-5.53552270e-01 -9.26570833e-01 -1.56623733e+00 -8.27062845e-01
-2.69037545e-01 8.14089477e-01 -1.23933502e-01 2.42854565... | [4.374209403991699, 2.8535308837890625] |
aa9de4a5-6c59-4272-912c-61fa705bfd1c | what-can-an-accent-identifier-learn-probing | 2306.06524 | null | https://arxiv.org/abs/2306.06524v1 | https://arxiv.org/pdf/2306.06524v1.pdf | What Can an Accent Identifier Learn? Probing Phonetic and Prosodic Information in a Wav2vec2-based Accent Identification Model | This study is focused on understanding and quantifying the change in phoneme and prosody information encoded in the Self-Supervised Learning (SSL) model, brought by an accent identification (AID) fine-tuning task. This problem is addressed based on model probing. Specifically, we conduct a systematic layer-wise analysi... | ['John H. L. Hansen', 'Okim Kang', 'Ram C. M. C. Shekar', 'Mu Yang'] | 2023-06-10 | null | null | null | null | ['prosody-prediction'] | ['natural-language-processing'] | [ 8.58604684e-02 1.48084044e-01 -4.15547192e-01 -5.92758954e-01
-7.44877696e-01 -8.11568677e-01 5.81579149e-01 8.02633986e-02
-3.84845287e-01 3.12547445e-01 9.11516011e-01 -4.89471033e-02
1.28749490e-01 -3.80483776e-01 -5.98635674e-01 -5.04876912e-01
1.26988798e-01 2.10299343e-01 -9.94179398e-02 -4.10251647... | [14.405254364013672, 6.842644691467285] |
b8ead6c2-32b5-45e9-98f5-5c8856166e3a | latent-tree-decomposition-parsers-for-amr-to | 2108.12304 | null | https://arxiv.org/abs/2108.12304v2 | https://arxiv.org/pdf/2108.12304v2.pdf | Latent Tree Decomposition Parsers for AMR-to-Text Generation | Graph encoders in AMR-to-text generation models often rely on neighborhood convolutions or global vertex attention. While these approaches apply to general graphs, AMRs may be amenable to encoders that target their tree-like structure. By clustering edges into a hierarchy, a tree decomposition summarizes graph structur... | ['Daniel Gildea', 'Lisa Jin'] | 2021-08-27 | null | null | null | null | ['tree-decomposition'] | ['graphs'] | [ 6.30074680e-01 1.10168505e+00 -4.08142298e-01 -2.64894694e-01
-6.52549028e-01 -7.47551382e-01 6.56237900e-01 5.36680341e-01
7.59030506e-02 8.55892122e-01 5.50455570e-01 -8.05978775e-01
2.97013491e-01 -1.43559754e+00 -9.46375668e-01 -3.37743163e-01
-2.83488631e-01 5.48767328e-01 -7.64573291e-02 -2.83684265... | [10.201780319213867, 8.226901054382324] |
829b9569-5681-4b5d-89a7-75cd0c891e6f | perceptual-loss-based-speech-denoising-with | 2010.1186 | null | http://arxiv.org/abs/2010.11860v1 | http://arxiv.org/pdf/2010.11860v1.pdf | Perceptual Loss based Speech Denoising with an ensemble of Audio Pattern Recognition and Self-Supervised Models | Deep learning based speech denoising still suffers from the challenge of
improving perceptual quality of enhanced signals. We introduce a generalized
framework called Perceptual Ensemble Regularization Loss (PERL) built on the
idea of perceptual losses. Perceptual loss discourages distortion to certain
speech propertie... | [] | 2020-10-22 | perceptual-loss-based-speech-denoising-with-1 | https://ieeexplore.ieee.org/abstract/document/9413555 | https://arxiv.org/pdf/2010.11860.pdf | null | ['speech-denoising'] | ['speech'] | [ 4.13062423e-01 -3.13085131e-02 2.29952350e-01 -3.76461923e-01
-1.43417931e+00 -3.01290780e-01 6.70164227e-01 2.34481692e-02
-6.37351990e-01 3.41231138e-01 7.80225694e-01 -1.78344250e-01
-5.62733486e-02 -1.92875654e-01 -7.55279243e-01 -6.36893630e-01
-5.13510592e-02 -5.48919439e-02 4.79480512e-02 -5.15595675... | [15.078836441040039, 5.848421573638916] |
67574efd-0b84-4a1f-873c-0e0a97a4c0a4 | pmatch-paired-masked-image-modeling-for-dense | 2303.17342 | null | https://arxiv.org/abs/2303.17342v1 | https://arxiv.org/pdf/2303.17342v1.pdf | PMatch: Paired Masked Image Modeling for Dense Geometric Matching | Dense geometric matching determines the dense pixel-wise correspondence between a source and support image corresponding to the same 3D structure. Prior works employ an encoder of transformer blocks to correlate the two-frame features. However, existing monocular pretraining tasks, e.g., image classification, and maske... | ['Xiaoming Liu', 'Shengjie Zhu'] | 2023-03-30 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhu_PMatch_Paired_Masked_Image_Modeling_for_Dense_Geometric_Matching_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhu_PMatch_Paired_Masked_Image_Modeling_for_Dense_Geometric_Matching_CVPR_2023_paper.pdf | cvpr-2023-1 | ['geometric-matching'] | ['computer-vision'] | [ 5.31338453e-01 1.10285193e-01 -2.75875837e-01 -5.23569942e-01
-8.12814116e-01 -2.32054561e-01 5.04905045e-01 -3.98661256e-01
4.04212587e-02 3.37998927e-01 1.29905239e-01 -1.11923911e-01
2.62623131e-01 -8.37565780e-01 -1.17568493e+00 -5.48126042e-01
5.62717140e-01 6.13409467e-02 2.10307673e-01 1.14694238... | [8.730473518371582, -2.3664588928222656] |
4b9d676d-86b9-4f6f-ac98-2fc2f548925f | competition-based-resilience-in-distributed | 2203.14099 | null | https://arxiv.org/abs/2203.14099v3 | https://arxiv.org/pdf/2203.14099v3.pdf | Competition-Based Resilience in Distributed Quadratic Optimization | This paper proposes a novel approach to resilient distributed optimization with quadratic costs in a networked control system (e.g., wireless sensor network, power grid, robotic team) prone to external attacks (e.g., hacking, power outage) that cause agents to misbehave. Departing from classical filtering strategies pr... | ['Luca Schenato', 'Jeff S. Shamma', 'Giacomo Como', 'Luca Ballotta'] | 2022-03-26 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [-7.77391940e-02 2.42808089e-01 3.43397826e-01 5.70311129e-01
-2.09793165e-01 -1.17685568e+00 4.73897427e-01 2.36968920e-01
-2.74461687e-01 7.79811680e-01 -1.07531194e-02 -4.38617527e-01
-8.13982725e-01 -7.56814659e-01 -4.02561128e-01 -9.75377262e-01
-7.30997145e-01 7.18374876e-03 2.29588240e-01 -6.59964979... | [4.605893611907959, 2.795121669769287] |
40726cd6-f2fa-4eac-ac7f-9179d3f76759 | a-multimodal-dynamical-variational | 2305.03582 | null | https://arxiv.org/abs/2305.03582v1 | https://arxiv.org/pdf/2305.03582v1.pdf | A Multimodal Dynamical Variational Autoencoder for Audiovisual Speech Representation Learning | In this paper, we present a multimodal \textit{and} dynamical VAE (MDVAE) applied to unsupervised audio-visual speech representation learning. The latent space is structured to dissociate the latent dynamical factors that are shared between the modalities from those that are specific to each modality. A static latent v... | ['Renaud Séguier', 'Xavier Alameda-Pineda', 'Laurent Girin', 'Simon Leglaive', 'Samir Sadok'] | 2023-05-05 | null | null | null | null | ['speech-emotion-recognition'] | ['speech'] | [ 1.3401705e-01 -1.5493090e-02 -1.5233368e-01 -3.1358910e-01
-6.7539191e-01 -6.3466507e-01 7.4985093e-01 -2.1589148e-01
-2.9266748e-01 1.2199735e-01 5.2126205e-01 -4.5715183e-02
2.4410667e-01 -2.8039744e-01 -6.2905073e-01 -1.1606362e+00
-7.7685136e-03 2.1014677e-01 -1.8055092e-01 -1.5898213e-02
-3.9095971e-01... | [14.207903861999512, 5.182390213012695] |
f9486f6f-896f-45a6-81ef-0589e7acae4b | 191013439 | 1910.13439 | null | https://arxiv.org/abs/1910.13439v2 | https://arxiv.org/pdf/1910.13439v2.pdf | Learning to Manipulate Deformable Objects without Demonstrations | In this paper we tackle the problem of deformable object manipulation through model-free visual reinforcement learning (RL). In order to circumvent the sample inefficiency of RL, we propose two key ideas that accelerate learning. First, we propose an iterative pick-place action space that encodes the conditional relati... | ['Pieter Abbeel', 'Yilin Wu', 'Thanard Kurutach', 'Wilson Yan', 'Lerrel Pinto'] | 2019-10-29 | null | null | null | null | ['deformable-object-manipulation'] | ['robots'] | [ 2.55947381e-01 1.61738291e-01 -4.35463935e-01 -3.43176462e-02
-7.70789802e-01 -9.24942851e-01 3.08751017e-01 1.17438152e-01
-5.81657231e-01 9.29536104e-01 -6.15102500e-02 1.90730747e-02
-3.48510481e-02 -7.01865613e-01 -1.52549982e+00 -8.78616750e-01
-2.96541154e-01 7.82981277e-01 2.19765365e-01 1.18011674... | [4.726578235626221, 0.6230683326721191] |
86c38389-d929-442c-8d6b-decc58235347 | combining-sequence-distillation-and-transfer | null | null | https://aclanthology.org/2020.wmt-1.61 | https://aclanthology.org/2020.wmt-1.61.pdf | Combining Sequence Distillation and Transfer Learning for Efficient Low-Resource Neural Machine Translation Models | In neural machine translation (NMT), sequence distillation (SD) through creation of distilled corpora leads to efficient (compact and fast) models. However, its effectiveness in extremely low-resource (ELR) settings has not been well-studied. On the other hand, transfer learning (TL) by leveraging larger helping corpor... | ['Atsushi Fujita', 'Raj Dabre'] | null | null | null | null | wmt-emnlp-2020-11 | ['low-resource-neural-machine-translation'] | ['natural-language-processing'] | [ 2.90713400e-01 5.08700078e-03 -3.76106948e-01 -3.48245770e-01
-1.63549638e+00 -7.47948289e-01 4.46855009e-01 -3.28978747e-01
-9.55110848e-01 1.22406662e+00 2.37270162e-01 -1.13488638e+00
4.22375590e-01 -2.69322693e-01 -8.28993022e-01 -3.86164576e-01
4.01218295e-01 9.46429610e-01 -4.07185495e-01 -4.97302353... | [11.639324188232422, 10.283758163452148] |
ca3fea96-f7d2-4508-986e-7ca0e542dc5a | uzbek-affix-finite-state-machine-for-stemming | 2205.10078 | null | https://arxiv.org/abs/2205.10078v1 | https://arxiv.org/pdf/2205.10078v1.pdf | Uzbek affix finite state machine for stemming | This work presents a morphological analyzer for the Uzbek language using a finite state machine. The proposed methodology is a morphologic analysis of Uzbek words by using an affix striping to find a root and without including any lexicon. This method helps to perform morphological analysis of words from a large amount... | ['Ulugbek Salaev', 'Maksud Sharipov'] | 2022-05-20 | null | null | null | null | ['morphological-analysis'] | ['natural-language-processing'] | [-2.29840167e-02 -2.95413714e-02 8.50833654e-02 -1.41619250e-01
8.38246942e-02 -7.93912292e-01 6.59356534e-01 7.35391796e-01
-8.34146738e-01 7.96074092e-01 -1.62121162e-01 -8.98822069e-01
9.09583718e-02 -1.18085134e+00 -1.89809263e-01 -5.48821807e-01
2.53184229e-01 7.23809421e-01 4.97779489e-01 -5.56642711... | [10.39012622833252, 10.205150604248047] |
7edfd40f-f384-4209-95dc-8dd6733f90c4 | online-multi-object-tracking-with-delta-glmb | 2011.10111 | null | https://arxiv.org/abs/2011.10111v2 | https://arxiv.org/pdf/2011.10111v2.pdf | Online Multi-Object Tracking with delta-GLMB Filter based on Occlusion and Identity Switch Handling | In this paper, we propose an online multi-object tracking (MOT) method in a delta Generalized Labeled Multi-Bernoulli (delta-GLMB) filter framework to address occlusion and miss-detection issues, reduce false alarms, and recover identity switch (ID switch). To handle occlusion and miss-detection issues, we propose a me... | ['Mohammad Ali Masnadi-Shirazi', 'Mohammadjavad Abbaspour'] | 2020-11-19 | null | null | null | null | ['online-multi-object-tracking'] | ['computer-vision'] | [-1.01628572e-01 -5.79341114e-01 3.81792672e-02 -6.37477934e-02
-5.84933221e-01 -4.46812510e-01 3.80823821e-01 3.08797956e-01
-5.45721292e-01 9.52611983e-01 -2.04423681e-01 -2.23371666e-03
-7.41056800e-02 -6.95552111e-01 -7.83162355e-01 -8.79835963e-01
-1.31928071e-01 5.56233466e-01 1.00300133e+00 2.18011335... | [6.47983455657959, -2.0075182914733887] |
6ca56e04-59a6-4c34-93f9-35a27b6a759f | a-deep-learning-approach-for-digital | 2202.0527 | null | https://arxiv.org/abs/2202.05270v2 | https://arxiv.org/pdf/2202.05270v2.pdf | A Deep Learning Approach for Digital Color Reconstruction of Lenticular Films | We propose the first accurate digitization and color reconstruction process for historical lenticular film that is robust to artifacts. Lenticular films emerged in the 1920s and were one of the first technologies that permitted to capture full color information in motion. The technology leverages an RGB filter and cyli... | ['Jan Dirk Wegner', 'David Pfluger', 'Giorgio Trumpy', "Stefano D'Aronco"] | 2022-02-10 | null | null | null | null | ['colorization'] | ['computer-vision'] | [ 2.95841336e-01 -3.48043650e-01 2.72355348e-01 -2.00160176e-01
-5.05961299e-01 -8.97031665e-01 3.92646194e-01 -4.35477167e-01
-3.06333393e-01 6.04443252e-01 -1.73034176e-01 -2.76648462e-01
3.23527485e-01 -7.40968406e-01 -9.59489763e-01 -3.40451717e-01
3.11702251e-01 7.52911195e-02 3.97631794e-01 -2.80402601... | [10.89099407196045, -1.5974209308624268] |
d47b00cd-734c-43c9-8ff7-e1584fad0e6e | generalizing-adam-to-manifolds-for | 2305.16901 | null | https://arxiv.org/abs/2305.16901v1 | https://arxiv.org/pdf/2305.16901v1.pdf | Generalizing Adam To Manifolds For Efficiently Training Transformers | One of the primary reasons behind the success of neural networks has been the emergence of an array of new, highly-successful optimizers, perhaps most importantly the Adam optimizer. It is wiedely used for training neural networks, yet notoriously hard to interpret. Lacking a clear physical intuition, Adam is difficult... | ['Benedikt Brantner'] | 2023-05-26 | null | null | null | null | ['physical-intuition'] | ['reasoning'] | [-6.31938875e-02 2.45077625e-01 -7.65343010e-02 -1.19512998e-01
7.66822994e-02 -6.34482265e-01 7.65338719e-01 -1.22545625e-03
-6.01576746e-01 5.97836971e-01 -1.67437896e-01 -4.28350955e-01
-2.96517134e-01 -4.92748320e-01 -7.45814502e-01 -1.10295737e+00
-3.06477368e-01 3.83468896e-01 3.23697999e-02 -6.23432755... | [7.719864845275879, 3.812159538269043] |
fbf2d7d6-077b-4466-bbb4-a2cdee173d17 | is-your-code-generated-by-chatgpt-really | 2305.0121 | null | https://arxiv.org/abs/2305.01210v2 | https://arxiv.org/pdf/2305.01210v2.pdf | Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation | Program synthesis has been long studied with recent approaches focused on directly using the power of Large Language Models (LLMs) to generate code. Programming benchmarks, with curated synthesis problems and test-cases, are used to measure the performance of various LLMs on code synthesis. However, these test-cases ca... | ['Lingming Zhang', 'Yuyao Wang', 'Chunqiu Steven Xia', 'Jiawei Liu'] | 2023-05-02 | null | null | null | null | ['program-synthesis'] | ['computer-code'] | [ 1.28083527e-01 1.55557036e-01 -2.97283232e-01 -1.82288080e-01
-9.97056425e-01 -9.04624224e-01 4.43280011e-01 2.45410368e-01
1.94316015e-01 6.17231071e-01 -1.31886348e-01 -8.91298354e-01
2.93208569e-01 -1.15667403e+00 -1.10003686e+00 -3.98793183e-02
1.31251231e-01 1.84513003e-01 4.65167165e-01 -4.29179311... | [7.79813814163208, 7.636441707611084] |
6fb38d9d-19d9-4c63-8d3b-274ba24531b5 | cross-modal-contrastive-learning-for-1 | 2302.14057 | null | https://arxiv.org/abs/2302.14057v1 | https://arxiv.org/pdf/2302.14057v1.pdf | Cross-modal Contrastive Learning for Multimodal Fake News Detection | Automatic detection of multimodal fake news has gained a widespread attention recently. Many existing approaches seek to fuse unimodal features to produce multimodal news representations. However, the potential of powerful cross-modal contrastive learning methods for fake news detection has not been well exploited. Bes... | ['Siqi Wang', 'Xiaohan Xu', 'Shuai Zhang', 'Hongbo Xu', 'Chuang Zhang', 'Longzheng Wang'] | 2023-02-25 | null | null | null | null | ['open-question'] | ['natural-language-processing'] | [ 4.89407107e-02 -3.46579701e-01 -2.58207321e-01 -4.27770704e-01
-1.34101367e+00 -3.23510557e-01 1.12963939e+00 1.76517442e-01
-2.80644089e-01 2.64504254e-01 4.00640070e-01 1.10540632e-02
2.74943709e-01 -3.13038230e-01 -5.90118289e-01 -7.26618528e-01
3.84106755e-01 1.21371582e-01 -4.25158776e-02 -3.64304394... | [8.165167808532715, 10.301095008850098] |
bf2bb55b-ce3c-4158-88c3-e6ec46d12792 | causal-discovery-in-probabilistic-networks | 2208.04627 | null | https://arxiv.org/abs/2208.04627v2 | https://arxiv.org/pdf/2208.04627v2.pdf | Causal Discovery in Probabilistic Networks with an Identifiable Causal Effect | Causal identification is at the core of the causal inference literature, where complete algorithms have been proposed to identify causal queries of interest. The validity of these algorithms hinges on the restrictive assumption of having access to a correctly specified causal structure. In this work, we study the setti... | ['Negar Kiyavash', 'Jalal Etesami', 'Matthew J. Vowels', 'Ehsan Mokhtarian', 'Fateme Jamshidi', 'Sina Akbari'] | 2022-08-09 | null | null | null | null | ['causal-identification'] | ['reasoning'] | [ 5.66764235e-01 6.71391666e-01 -5.50844312e-01 -3.00008565e-01
-4.93880540e-01 -6.88179016e-01 4.53640580e-01 5.74939907e-01
1.44039486e-02 9.63384092e-01 2.53638387e-01 -6.77713454e-01
-8.02126765e-01 -1.17060256e+00 -1.04362142e+00 -5.90145767e-01
-4.95526969e-01 5.74525952e-01 2.31115282e-01 3.55598599... | [7.727264404296875, 5.3139238357543945] |
089f0f74-a743-405a-b3db-49b70fe9ed61 | metabolic-regulatory-network-kinetic-modeling | 2305.00165 | null | https://arxiv.org/abs/2305.00165v1 | https://arxiv.org/pdf/2305.00165v1.pdf | Metabolic Regulatory Network Kinetic Modeling with Multiple Isotopic Tracers for iPSCs | The rapidly expanding market for regenerative medicines and cell therapies highlights the need to advance the understanding of cellular metabolisms and improve the prediction of cultivation production process for human induced pluripotent stem cells (iPSCs). In this paper, a metabolic kinetic model was developed to cha... | ['Sarah W. Harcum', 'Wei Xie', 'Keqi Wang'] | 2023-04-29 | null | null | null | null | ['culture'] | ['speech'] | [ 5.50723784e-02 -6.38342917e-01 -4.04022783e-01 4.36393052e-01
1.84404224e-01 -9.09105957e-01 5.39379060e-01 5.63637257e-01
-4.33782227e-02 9.27035213e-01 3.08487147e-01 -2.15626478e-01
1.41072392e-01 -7.59430349e-01 -1.85546443e-01 -1.02762747e+00
3.82568479e-01 5.98210990e-01 -2.71089762e-01 2.11507201... | [5.8332438468933105, 4.33102560043335] |
049ac2e5-47fc-4b8f-bf06-fb1832ba8b97 | qu-apporte-bert-a-l-analyse-syntaxique-en | null | null | https://aclanthology.org/2020.jeptalnrecital-taln.17 | https://aclanthology.org/2020.jeptalnrecital-taln.17.pdf | Qu'apporte BERT \`a l'analyse syntaxique en constituants discontinus ? Une suite de tests pour \'evaluer les pr\'edictions de structures syntaxiques discontinues en anglais (What does BERT contribute to discontinuous constituency parsing ? A test suite to evaluate discontinuous constituency structure predictions in Eng... | Cet article propose d{'}analyser les apports d{'}un mod{\`e}le de langue pr{\'e}-entra{\^\i}n{\'e} de type BERT (bidirectional encoder representations from transformers) {\`a} l{'}analyse syntaxique en constituants discontinus en anglais (PTB, Penn Treebank). Pour cela, nous r{\'e}alisons une comparaison des erreurs d{... | ['Maximin Coavoux'] | 2020-06-01 | null | null | null | jeptalnrecital-2020-6 | ['constituency-parsing'] | ['natural-language-processing'] | [ 1.69605955e-01 5.89755952e-01 3.58089358e-01 -3.81891996e-01
-6.91698790e-01 -1.15383506e+00 4.61797714e-01 5.09046912e-01
-7.30283976e-01 9.52090204e-01 -2.28629321e-01 -5.43127418e-01
-4.01234865e-01 -1.08617783e+00 -7.34059870e-01 -4.77577031e-01
-3.66381854e-01 5.60198963e-01 4.16306674e-01 -7.22652614... | [14.100536346435547, 13.315065383911133] |
4b09bf06-d26c-4bbf-a410-90b6ce500069 | confit-toward-faithful-dialogue-summarization-1 | null | null | https://openreview.net/forum?id=cjUocqbaCcF | https://openreview.net/pdf?id=cjUocqbaCcF | CONFIT: Toward Faithful Dialogue Summarization with Linguistically-Informed Contrastive Fine-tuning | Factual inconsistencies in generated summaries severely limit the practical applications of abstractive dialogue summarization. Although significant progress has been achieved by using pre-trained neural language models, substantial amounts of hallucinated content are found during the human evaluation. In this work, we... | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['meeting-summarization'] | ['natural-language-processing'] | [ 1.62491024e-01 7.87275195e-01 1.92316975e-02 -5.23137629e-01
-1.40154517e+00 -4.85079050e-01 8.58423531e-01 4.00922090e-01
-2.87253112e-01 1.17591465e+00 1.03715253e+00 1.30332038e-01
3.35369855e-01 -2.98102349e-01 -4.79725391e-01 -7.91259184e-02
1.69353336e-01 6.70609117e-01 -4.54731612e-03 -5.35247445... | [12.349945068359375, 9.224235534667969] |
bd933a94-69c7-4561-a669-fb57eff41a63 | emocaps-emotion-capsule-based-model-for | 2203.13504 | null | https://arxiv.org/abs/2203.13504v1 | https://arxiv.org/pdf/2203.13504v1.pdf | EmoCaps: Emotion Capsule based Model for Conversational Emotion Recognition | Emotion recognition in conversation (ERC) aims to analyze the speaker's state and identify their emotion in the conversation. Recent works in ERC focus on context modeling but ignore the representation of contextual emotional tendency. In order to extract multi-modal information and the emotional tendency of the uttera... | ['Yusen Zhu', 'Ming Zhao', 'Fengxiao Tang', 'Zaijing Li'] | 2022-03-25 | null | https://aclanthology.org/2022.findings-acl.126 | https://aclanthology.org/2022.findings-acl.126.pdf | findings-acl-2022-5 | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [-3.02941978e-01 -3.67461979e-01 3.36577356e-01 -8.29036117e-01
-2.48042047e-01 -2.25515798e-01 1.47852406e-01 -1.52192116e-01
-3.85906488e-01 1.22195534e-01 8.38123322e-01 9.35123339e-02
5.25641561e-01 -3.25890571e-01 2.17964396e-01 -4.73128200e-01
3.80474061e-01 -4.61458266e-01 -2.65493840e-01 -4.71305072... | [13.069226264953613, 5.930931091308594] |
44a1e089-012b-4f7e-b1ed-9d0ec5e01d2b | automating-cell-counting-in-fluorescent | null | null | https://www.nature.com/articles/s41598-021-01929-5#Abs1 | https://rdcu.be/cB1Ds | Automating cell counting in fluorescent microscopy through deep learning with c-ResUnet | Counting cells in fluorescent microscopy is a tedious, time-consuming task that researchers have to accomplish to assess the effects of different experimental conditions on biological structures of interest. Although such objects are generally easy to identify, the process of manually annotating cells is sometimes subj... | ['Antonio', 'Fabio and Zoccoli', 'Lorenzo and Squarcio', 'Marco and Rinaldi', 'Timna and Luppi', 'Matteo and Hitrec', 'Roberto and Cerri', 'Luca and Amici', 'Roberto and Clissa', 'Morelli'] | 2021-11-25 | null | null | null | scientific-reports-2021-11 | ['object-counting', '2d-semantic-segmentation'] | ['computer-vision', 'computer-vision'] | [ 3.38311911e-01 -1.11658446e-01 5.07264495e-01 -1.67781487e-01
-5.36181390e-01 -7.52817929e-01 4.46706831e-01 6.44048929e-01
-1.14357626e+00 9.95538712e-01 -3.23076159e-01 -1.98848903e-01
1.87833190e-01 -4.17680591e-01 -7.85528481e-01 -9.84989762e-01
7.87339360e-02 4.84369338e-01 2.39821985e-01 3.32675695... | [14.685964584350586, -3.1470234394073486] |
f515fdf9-c671-46f1-b5a9-5196c859c046 | attribution-scores-and-causal-counterfactuals | 2303.02829 | null | https://arxiv.org/abs/2303.02829v2 | https://arxiv.org/pdf/2303.02829v2.pdf | Attribution-Scores and Causal Counterfactuals as Explanations in Artificial Intelligence | In this expository article we highlight the relevance of explanations for artificial intelligence, in general, and for the newer developments in {\em explainable AI}, referring to origins and connections of and among different approaches. We describe in simple terms, explanations in data management and machine learning... | ['Leopoldo Bertossi'] | 2023-03-06 | null | null | null | null | ['logical-reasoning'] | ['reasoning'] | [ 4.08600628e-01 9.34198737e-01 -6.84230328e-01 -6.27917767e-01
9.62898806e-02 -2.76998967e-01 8.92481089e-01 3.49936843e-01
-9.41232890e-02 1.22144961e+00 7.28123426e-01 -8.08744073e-01
-9.77247238e-01 -6.52705431e-01 -4.85303581e-01 -3.64699781e-01
-3.14561307e-01 5.97153604e-01 -5.70166349e-01 -3.84510234... | [8.664872169494629, 5.682901382446289] |
158e8cf6-0750-4d6d-a80d-515dbc2fc454 | dissecting-arbitrary-scale-super-resolution | 2306.00714 | null | https://arxiv.org/abs/2306.00714v1 | https://arxiv.org/pdf/2306.00714v1.pdf | Dissecting Arbitrary-scale Super-resolution Capability from Pre-trained Diffusion Generative Models | Diffusion-based Generative Models (DGMs) have achieved unparalleled performance in synthesizing high-quality visual content, opening up the opportunity to improve image super-resolution (SR) tasks. Recent solutions for these tasks often train architecture-specific DGMs from scratch, or require iterative fine-tuning and... | ['Zhenhua Han', 'Yifei Shen', 'Xinyang Jiang', 'Jingcai Guo', 'Jie Zhang', 'Song Guo', 'Qihua Zhou', 'Ruibin Li'] | 2023-06-01 | null | null | null | null | ['image-super-resolution'] | ['computer-vision'] | [ 6.63917065e-01 -1.07152656e-01 2.67022736e-02 -1.61001086e-01
-8.83479714e-01 -4.01643455e-01 7.10330307e-01 -2.57884651e-01
-2.53876895e-01 6.75860465e-01 1.81565911e-01 -8.96033123e-02
-2.52165973e-01 -8.21940899e-01 -6.06699228e-01 -8.11899245e-01
4.90807146e-02 1.57097250e-01 4.83914673e-01 -3.91254187... | [11.208745002746582, -1.9269622564315796] |
33799f05-bea7-489f-af75-3485b8887941 | reflectance-hashing-for-material-recognition | 1502.02092 | null | http://arxiv.org/abs/1502.02092v1 | http://arxiv.org/pdf/1502.02092v1.pdf | Reflectance Hashing for Material Recognition | We introduce a novel method for using reflectance to identify materials.
Reflectance offers a unique signature of the material but is challenging to
measure and use for recognizing materials due to its high-dimensionality. In
this work, one-shot reflectance is captured using a unique optical camera
measuring {\it refle... | ['Kristin Dana', 'Ko Nishino', 'Hang Zhang'] | 2015-02-07 | reflectance-hashing-for-material-recognition-1 | http://openaccess.thecvf.com/content_cvpr_2015/html/Zhang_Reflectance_Hashing_for_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Zhang_Reflectance_Hashing_for_2015_CVPR_paper.pdf | cvpr-2015-6 | ['material-recognition'] | ['computer-vision'] | [ 6.25516176e-01 -6.77266777e-01 -2.18695581e-01 -1.11769237e-01
-7.05942631e-01 -6.01984739e-01 2.41184130e-01 -1.96854040e-01
2.01868221e-01 1.26415659e-02 9.63480622e-02 3.13137323e-01
4.69493791e-02 -1.00480580e+00 -4.71528679e-01 -8.78451109e-01
3.40138555e-01 3.37967277e-01 1.04969777e-01 1.23111516... | [9.766813278198242, -2.840467929840088] |
a097db13-5b3b-4a11-9c1b-b5c19b111b99 | contextual-blocking-bandits | 2003.03426 | null | https://arxiv.org/abs/2003.03426v2 | https://arxiv.org/pdf/2003.03426v2.pdf | Contextual Blocking Bandits | We study a novel variant of the multi-armed bandit problem, where at each time step, the player observes an independently sampled context that determines the arms' mean rewards. However, playing an arm blocks it (across all contexts) for a fixed and known number of future time steps. The above contextual setting, which... | ['Sanjay Shakkottai', 'Constantine Caramanis', 'Soumya Basu', 'Orestis Papadigenopoulos'] | 2020-03-06 | null | null | null | null | ['novel-concepts'] | ['reasoning'] | [ 2.81812310e-01 2.19021961e-01 -7.60964811e-01 -7.91047662e-02
-9.21300352e-01 -1.03594661e+00 -1.15255676e-01 4.73028794e-02
-6.29437447e-01 1.00779545e+00 -6.57084584e-02 -7.69105673e-01
-9.14880812e-01 -8.25281322e-01 -1.12737501e+00 -9.82698739e-01
-2.24564731e-01 1.00714421e+00 -5.03575467e-02 1.05524138... | [4.565964221954346, 3.350005626678467] |
f798f114-497f-45c8-9635-58be42c6e5c0 | entity-type-prediction-leveraging-graph-walks | 2207.14094 | null | https://arxiv.org/abs/2207.14094v2 | https://arxiv.org/pdf/2207.14094v2.pdf | Entity Type Prediction Leveraging Graph Walks and Entity Descriptions | The entity type information in Knowledge Graphs (KGs) such as DBpedia, Freebase, etc. is often incomplete due to automated generation or human curation. Entity typing is the task of assigning or inferring the semantic type of an entity in a KG. This paper presents \textit{GRAND}, a novel approach for entity typing leve... | ['Mehwish Alam', 'Harald Sack', 'Heiko Paulheim', 'Jan Portisch', 'Russa Biswas'] | 2022-07-28 | null | null | null | null | ['type-prediction', 'entity-typing'] | ['computer-code', 'natural-language-processing'] | [-5.71636379e-01 4.46493477e-01 -2.23540097e-01 -4.84343320e-01
-1.18044987e-01 -6.61867619e-01 6.99182391e-01 8.20117652e-01
-6.71147645e-01 8.80244315e-01 4.51382577e-01 -1.44215003e-01
-2.02452421e-01 -1.61129713e+00 -8.70139837e-01 -2.99312472e-01
-4.02313471e-01 7.67821312e-01 4.59243625e-01 -5.69970369... | [9.103018760681152, 8.127575874328613] |
2a2d6d16-b53b-44a8-9cc3-5a15bbb7da96 | principal-component-classification | 2210.12746 | null | https://arxiv.org/abs/2210.12746v2 | https://arxiv.org/pdf/2210.12746v2.pdf | Principal Component Classification | We propose to directly compute classification estimates by learning features encoded with their class scores using PCA. Our resulting model has a encoder-decoder structure suitable for supervised learning, it is computationally efficient and performs well for classification on several datasets. | ['Rozenn Dahyot'] | 2022-10-23 | null | null | null | null | ['component-classification'] | ['natural-language-processing'] | [ 4.60674852e-01 1.34135932e-01 -7.63161421e-01 -1.05721414e+00
-1.41808152e+00 -3.50175947e-01 8.28986883e-01 1.66832402e-01
-4.10951912e-01 8.10131371e-01 2.80003279e-01 -5.62691949e-02
-7.63429701e-02 -5.84005892e-01 -5.97683132e-01 -6.28756762e-01
-2.75308639e-01 6.75104439e-01 -7.40156099e-02 5.16240358... | [9.425410270690918, 2.8440749645233154] |
74c9da47-fefd-4fda-8ff7-1be80300055a | mulda-a-multilingual-data-augmentation | null | null | https://aclanthology.org/2021.acl-long.453 | https://aclanthology.org/2021.acl-long.453.pdf | MulDA: A Multilingual Data Augmentation Framework for Low-Resource Cross-Lingual NER | Named Entity Recognition (NER) for low-resource languages is a both practical and challenging research problem. This paper addresses zero-shot transfer for cross-lingual NER, especially when the amount of source-language training data is also limited. The paper first proposes a simple but effective labeled sequence tra... | ['Chunyan Miao', 'Luo Si', 'Shafiq Joty', 'Lidong Bing', 'Bosheng Ding', 'Linlin Liu'] | 2021-08-01 | null | null | null | acl-2021-5 | ['cross-lingual-ner'] | ['natural-language-processing'] | [-1.75652251e-01 -3.25902194e-01 -4.22032565e-01 -5.83271921e-01
-1.36816919e+00 -7.43343592e-01 4.97155637e-01 -3.62298876e-01
-9.25741553e-01 1.42238665e+00 2.94169992e-01 -2.76198298e-01
6.57650113e-01 -6.41628385e-01 -6.18760586e-01 -2.15312317e-01
3.20703447e-01 6.70655072e-01 -8.57153386e-02 -5.15942931... | [10.032968521118164, 9.720654487609863] |
86d5d5bc-d4e9-40a4-a940-177c68e7cb02 | analysis-of-optimal-portfolios-on-finite-and | 2302.06778 | null | https://arxiv.org/abs/2302.06778v1 | https://arxiv.org/pdf/2302.06778v1.pdf | Analysis of optimal portfolios on finite and small-time horizons for a multi-dimensional correlated stochastic volatility model | In this paper, we consider the portfolio optimization problem in a financial market where the underlying stochastic volatility model is driven by n-dimensional Brownian motions. At first, we derive a Hamilton-Jacobi-Bellman equation including the scaled covariances between the standard Brownian motions. We use an appro... | ['Indranil SenGupta', 'Minglian Lin'] | 2023-02-14 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [-4.35890645e-01 3.01160902e-01 3.22876155e-01 7.51851201e-02
-4.13005501e-01 -8.33793163e-01 3.43951195e-01 -1.19560502e-01
-6.00147545e-01 8.00707519e-01 -1.11360930e-01 -3.92100692e-01
-5.97952247e-01 -9.38101530e-01 -5.00756323e-01 -9.37123299e-01
-1.81912556e-01 4.18012619e-01 1.28878146e-01 -3.07849884... | [4.936863422393799, 3.9603147506713867] |
e091f354-0237-48f5-9764-efd13cf4e0ba | validate-on-sim-detect-on-real-model | 2111.00765 | null | https://arxiv.org/abs/2111.00765v3 | https://arxiv.org/pdf/2111.00765v3.pdf | Validate on Sim, Detect on Real -- Model Selection for Domain Randomization | A practical approach to learning robot skills, often termed sim2real, is to train control policies in simulation and then deploy them on a real robot. Popular techniques to improve the sim2real transfer build on domain randomization (DR) -- training the policy on a diverse set of randomly generated domains with the hop... | ['Aviv Tamar', 'Gal Novik', 'Shadi Endrawis', 'Guy Jacob', 'Gal Leibovich'] | 2021-11-01 | null | null | null | null | ['robotic-grasping'] | ['robots'] | [ 1.89628135e-02 -5.42163178e-02 -3.89632702e-01 -1.60578370e-01
-9.44990039e-01 -1.07183242e+00 6.47884488e-01 -1.67439610e-01
-7.34489918e-01 8.19378674e-01 -1.40808821e-01 -6.05643332e-01
-9.11306739e-02 -5.62840641e-01 -1.15196705e+00 -7.46037781e-01
-2.41138682e-01 9.78837132e-01 5.24341106e-01 -4.62320894... | [4.374575614929199, 1.4457868337631226] |
63472f63-d2bd-4d9b-bc7a-e9821dce352e | data-quality-as-predictor-of-voice-anti | 2103.14602 | null | https://arxiv.org/abs/2103.14602v2 | https://arxiv.org/pdf/2103.14602v2.pdf | Data Quality as Predictor of Voice Anti-Spoofing Generalization | Voice anti-spoofing aims at classifying a given utterance either as a bonafide human sample, or a spoofing attack (e.g. synthetic or replayed sample). Many anti-spoofing methods have been proposed but most of them fail to generalize across domains (corpora) -- and we do not know \emph{why}. We outline a novel interpret... | ['Tomi Kinnunen', 'Md Sahidullah', 'Rosa González Hautamäki', 'Bhusan Chettri'] | 2021-03-26 | null | null | null | null | ['voice-anti-spoofing'] | ['audio'] | [ 2.84461081e-01 -1.55231813e-02 -1.62294418e-01 -2.38671571e-01
-7.47235179e-01 -7.81295478e-01 9.56636548e-01 4.91261482e-03
-3.40188533e-01 3.69413108e-01 7.73571014e-01 -5.87460637e-01
-3.37386578e-02 -2.74625510e-01 -3.68526310e-01 -6.62401855e-01
-2.36438699e-02 2.53475726e-01 -1.10515423e-01 -2.25809410... | [14.078413963317871, 5.864184856414795] |
4846a4ad-de7f-48ee-af2d-984fdb6428ac | audio-driven-co-speech-gesture-video | 2212.0235 | null | https://arxiv.org/abs/2212.02350v1 | https://arxiv.org/pdf/2212.02350v1.pdf | Audio-Driven Co-Speech Gesture Video Generation | Co-speech gesture is crucial for human-machine interaction and digital entertainment. While previous works mostly map speech audio to human skeletons (e.g., 2D keypoints), directly generating speakers' gestures in the image domain remains unsolved. In this work, we formally define and study this challenging problem of ... | ['Ziwei Liu', 'Dahua Lin', 'Wayne Wu', 'Yuanqi Du', 'Hang Zhou', 'Qianyi Wu', 'Xian Liu'] | 2022-12-05 | null | null | null | null | ['video-generation'] | ['computer-vision'] | [ 3.15242767e-01 -1.40419900e-01 -2.20145881e-01 2.98188590e-02
-8.54953170e-01 -5.16271830e-01 8.66977274e-01 -8.24663758e-01
1.23571530e-01 1.92033753e-01 8.46169651e-01 -3.55730802e-02
1.16126053e-01 -3.72479171e-01 -5.64283252e-01 -8.17287445e-01
1.72457144e-01 2.88855489e-02 1.34518400e-01 -2.24124491... | [5.743698596954346, -0.1982276886701584] |
5b4f2bf7-d80c-415e-8f52-5ef8e1011b02 | semi-supervised-clustering-via-dynamic-graph | 2209.02513 | null | https://arxiv.org/abs/2209.02513v1 | https://arxiv.org/pdf/2209.02513v1.pdf | Semi-Supervised Clustering via Dynamic Graph Structure Learning | Most existing semi-supervised graph-based clustering methods exploit the supervisory information by either refining the affinity matrix or directly constraining the low-dimensional representations of data points. The affinity matrix represents the graph structure and is vital to the performance of semi-supervised graph... | ['Zuoqiang Shi', 'Xin Liang', 'Chenglong Bao', 'Huaming Ling'] | 2022-09-06 | null | null | null | null | ['graph-structure-learning'] | ['graphs'] | [-2.08062604e-01 3.91810201e-02 -4.06372488e-01 -4.67367470e-01
-4.40614432e-01 -5.14107764e-01 5.46802320e-02 2.87064701e-01
-1.92289904e-01 2.41494223e-01 1.88708022e-01 1.98174670e-01
-7.14260161e-01 -5.52752495e-01 -5.42768836e-01 -1.07023311e+00
6.63045198e-02 7.73911059e-01 -3.44239548e-02 9.09060463... | [7.769883632659912, 4.52731466293335] |
1b9ed547-b907-4d0b-ae18-155975800d2c | safeguarding-data-in-multimodal-ai-a | 2306.08173 | null | https://arxiv.org/abs/2306.08173v1 | https://arxiv.org/pdf/2306.08173v1.pdf | Safeguarding Data in Multimodal AI: A Differentially Private Approach to CLIP Training | The surge in multimodal AI's success has sparked concerns over data privacy in vision-and-language tasks. While CLIP has revolutionized multimodal learning through joint training on images and text, its potential to unintentionally disclose sensitive information necessitates the integration of privacy-preserving mechan... | ['Wanrong Zhang', 'Linjun Zhang', 'Ryumei Nakada', 'Peihan Liu', 'Alyssa Huang'] | 2023-06-13 | null | null | null | null | ['visual-question-answering-1', 'question-answering'] | ['computer-vision', 'natural-language-processing'] | [ 5.22253990e-01 1.99862927e-01 -1.81663454e-01 -7.22536683e-01
-1.38439739e+00 -8.99052680e-01 6.16923094e-01 2.50369668e-01
-8.23664904e-01 6.05473816e-01 1.74990982e-01 -4.01706398e-01
1.99674755e-01 -1.36950582e-01 -1.03605938e+00 -9.06248271e-01
1.39226586e-01 -6.73571229e-02 -4.44488108e-01 6.01986229... | [5.920570373535156, 6.749251365661621] |
55e2217c-59cc-44aa-891e-38611fbd8ecc | emotional-talking-head-generation-based-on | 2306.03594 | null | https://arxiv.org/abs/2306.03594v1 | https://arxiv.org/pdf/2306.03594v1.pdf | Emotional Talking Head Generation based on Memory-Sharing and Attention-Augmented Networks | Given an audio clip and a reference face image, the goal of the talking head generation is to generate a high-fidelity talking head video. Although some audio-driven methods of generating talking head videos have made some achievements in the past, most of them only focused on lip and audio synchronization and lack the... | ['Sen Li', 'Qi Li', 'Tianyi Xu', 'Li Liu', 'Yaxin Zhao', 'Jianrong Wang'] | 2023-06-06 | null | null | null | null | ['talking-head-generation'] | ['computer-vision'] | [-6.89536706e-02 2.32736394e-01 1.59111068e-01 -5.86955249e-01
-7.72862375e-01 5.69483899e-02 4.18966830e-01 -8.19117010e-01
6.27112314e-02 6.12438679e-01 5.89517951e-01 5.17244816e-01
3.73709202e-01 -2.84658074e-01 -5.72964311e-01 -8.20298135e-01
1.50600731e-01 1.61037734e-03 -3.14394861e-01 -2.34490391... | [13.246480941772461, -0.38782984018325806] |
22031173-176b-4cae-b2c3-617cef8c174a | imbedding-deep-neural-networks-1 | 2202.00113 | null | https://arxiv.org/abs/2202.00113v2 | https://arxiv.org/pdf/2202.00113v2.pdf | Imbedding Deep Neural Networks | Continuous-depth neural networks, such as Neural ODEs, have refashioned the understanding of residual neural networks in terms of non-linear vector-valued optimal control problems. The common solution is to use the adjoint sensitivity method to replicate a forward-backward pass optimisation problem. We propose a new ap... | ['Dmitry Kangin', 'Andrew Corbett'] | 2022-01-31 | imbedding-deep-neural-networks | https://openreview.net/forum?id=yKIAXjkJc2F | https://openreview.net/pdf?id=yKIAXjkJc2F | iclr-2022-4 | ['time-series-prediction'] | ['time-series'] | [ 2.21756086e-01 5.61952353e-01 2.70685758e-02 -1.09207556e-01
-2.41599053e-01 -5.66939116e-01 4.01111692e-01 -3.83724302e-01
-5.20873487e-01 7.23347247e-01 -3.85638028e-02 -5.83890557e-01
-4.18235511e-01 -4.59730864e-01 -8.43359232e-01 -1.05031705e+00
-4.68950689e-01 8.24788809e-02 -4.67311814e-02 -5.79523861... | [7.212666034698486, 3.548041582107544] |
b57e59cf-9479-4351-835c-734733f52502 | learning-to-recognize-3d-human-action-from-a | 1812.1055 | null | http://arxiv.org/abs/1812.10550v1 | http://arxiv.org/pdf/1812.10550v1.pdf | Learning to Recognize 3D Human Action from A New Skeleton-based Representation Using Deep Convolutional Neural Networks | Recognizing human actions in untrimmed videos is an important challenging
task. An effective 3D motion representation and a powerful learning model are
two key factors influencing recognition performance. In this paper we introduce
a new skeleton-based representation for 3D action recognition in videos. The
key idea of... | ['Sergio A. Velastin', 'Pablo Zegers', 'Huy-Hieu Pham', 'Alain Crouzil', 'Louahdi Khoudour'] | 2018-12-26 | null | null | null | null | ['3d-human-action-recognition'] | ['computer-vision'] | [ 3.82283449e-01 -3.74822378e-01 -3.82590085e-01 -2.65944172e-02
-1.90911844e-01 -1.82926789e-01 6.24706626e-01 -7.01447308e-01
-6.60158098e-01 2.75509626e-01 4.10017610e-01 -4.54245508e-02
1.75828710e-01 -4.52423781e-01 -8.11361015e-01 -7.31924474e-01
-2.10498959e-01 1.76479742e-01 5.47332168e-01 1.11651458... | [7.858481407165527, 0.38466569781303406] |
5c6f0b43-53db-4bfc-9cb0-a1ccaf67d705 | human-centric-spatio-temporal-video-grounding | 2011.05049 | null | https://arxiv.org/abs/2011.05049v2 | https://arxiv.org/pdf/2011.05049v2.pdf | Human-centric Spatio-Temporal Video Grounding With Visual Transformers | In this work, we introduce a novel task - Humancentric Spatio-Temporal Video Grounding (HC-STVG). Unlike the existing referring expression tasks in images or videos, by focusing on humans, HC-STVG aims to localize a spatiotemporal tube of the target person from an untrimmed video based on a given textural description. ... | ['Dong Xu', 'Qian Yu', 'Hongxu Jiang', 'Xiaojie Jin', 'Guanbin Li', 'Si Liu', 'Yue Liao', 'Zongheng Tang'] | 2020-11-10 | null | null | null | null | ['video-grounding', 'spatio-temporal-video-grounding'] | ['computer-vision', 'computer-vision'] | [ 4.63041104e-02 -4.01387602e-01 -1.84677899e-01 -3.09940845e-01
-9.32510734e-01 -3.87003690e-01 5.52851260e-01 1.73302472e-01
-4.46468830e-01 4.93019462e-01 3.68894279e-01 8.14791024e-02
1.73152000e-01 -4.72521007e-01 -7.22730219e-01 -5.53867817e-01
-6.73289225e-02 -1.62360460e-01 3.50165784e-01 -3.15259956... | [10.060369491577148, 0.7528606057167053] |
4cc3ad60-ba1b-4977-a688-febcf3c80aa8 | improving-a-sequence-to-sequence-nlp-model | 2212.14117 | null | https://arxiv.org/abs/2212.14117v1 | https://arxiv.org/pdf/2212.14117v1.pdf | Improving a sequence-to-sequence nlp model using a reinforcement learning policy algorithm | Nowadays, the current neural network models of dialogue generation(chatbots) show great promise for generating answers for chatty agents. But they are short-sighted in that they predict utterances one at a time while disregarding their impact on future outcomes. Modelling a dialogue's future direction is critical for g... | ['El ouaazizi Aziza', 'Aboulbichr Ahmed', 'Jabri Ismail'] | 2022-12-28 | null | null | null | null | ['policy-gradient-methods'] | ['methodology'] | [-1.77933320e-01 7.62249947e-01 1.97200805e-01 -4.84220266e-01
-2.32751131e-01 -4.37867552e-01 1.13157713e+00 -3.16825621e-02
-3.21944624e-01 1.30179393e+00 6.68956935e-01 -2.34712198e-01
1.31948784e-01 -9.55945849e-01 -8.29870850e-02 -3.45681518e-01
-1.63433865e-01 6.83306336e-01 -1.16129711e-01 -1.02324617... | [12.762392044067383, 8.187705039978027] |
31f34d26-e4e3-4e57-b773-0af131e200ae | facial-emotion-recognition-using-deep | 1910.11113 | null | https://arxiv.org/abs/1910.11113v1 | https://arxiv.org/pdf/1910.11113v1.pdf | Facial Emotion Recognition Using Deep Learning | We aim to construct a system that captures real-world facial images through the front camera on a laptop. The system is capable of processing/recognizing the captured image and predict a result in real-time. In this system, we exploit the power of deep learning technique to learn a facial emotion recognition (FER) mode... | ['Li-Heng Chen', 'Ching-Da Wu'] | 2019-10-19 | null | null | null | null | ['facial-emotion-recognition'] | ['computer-vision'] | [ 1.73087671e-01 -2.57196128e-01 1.72334403e-01 -1.23999166e+00
-1.52705327e-01 -1.69745460e-01 3.57565045e-01 -5.34918070e-01
-4.51692313e-01 4.93086755e-01 -3.00967366e-01 2.76778847e-01
4.08931643e-01 -8.69054556e-01 -5.66856146e-01 -3.11475843e-01
-2.50221759e-01 1.07375227e-01 -5.97722948e-01 -9.92862061... | [13.511208534240723, 1.7822997570037842] |
af3ba28d-08bb-481a-b7f3-29e2fb92dd1d | non-asymptotic-performance-of-social-machine | 2306.09397 | null | https://arxiv.org/abs/2306.09397v1 | https://arxiv.org/pdf/2306.09397v1.pdf | Non-Asymptotic Performance of Social Machine Learning Under Limited Data | This paper studies the probability of error associated with the social machine learning framework, which involves an independent training phase followed by a cooperative decision-making phase over a graph. This framework addresses the problem of classifying a stream of unlabeled data in a distributed manner. We conside... | ['Ali H. Sayed', 'Mert Kayaalp', 'Virginia Bordignon', 'Ping Hu'] | 2023-06-15 | null | null | null | null | ['classification-1'] | ['methodology'] | [ 3.60697627e-01 5.40166318e-01 -3.70080471e-01 -5.09404600e-01
-4.34203267e-01 -1.97222427e-01 3.60962868e-01 5.63911140e-01
-4.03163195e-01 8.61571789e-01 -5.55717647e-01 -2.74928451e-01
-4.73600298e-01 -7.56527603e-01 -7.98332810e-01 -1.20915103e+00
-1.06094368e-01 4.43478405e-01 6.78462610e-02 2.23841637... | [9.085372924804688, 4.129692077636719] |
8d462eed-9669-4f09-a150-9102d5349bba | addsl-hand-gesture-detection-and-sign | 2305.09736 | null | https://arxiv.org/abs/2305.09736v1 | https://arxiv.org/pdf/2305.09736v1.pdf | ADDSL: Hand Gesture Detection and Sign Language Recognition on Annotated Danish Sign Language | For a long time, detecting hand gestures and recognizing them as letters or numbers has been a challenging task. This creates communication barriers for individuals with disabilities. This paper introduces a new dataset, the Annotated Dataset for Danish Sign Language (ADDSL). Annota-tions for the dataset were made usin... | ['Sanyam Jain'] | 2023-05-16 | null | null | null | null | ['sign-language-recognition'] | ['computer-vision'] | [-1.43044502e-01 -1.73284918e-01 -1.50480211e-01 -2.23143116e-01
-5.92471659e-01 -5.03253400e-01 3.15864742e-01 -5.27750194e-01
-7.53657103e-01 8.29616785e-01 4.14064020e-01 -1.70306712e-02
2.51990378e-01 -2.18217254e-01 -5.38461030e-01 -5.56110919e-01
1.73165023e-01 4.86531287e-01 6.06383264e-01 1.57787457... | [9.107240676879883, -6.414835453033447] |
b8f2f9e3-04ed-4418-84bb-c2c2b6ebd3a9 | regularizing-contrastive-predictive-coding | 2304.05974 | null | https://arxiv.org/abs/2304.05974v2 | https://arxiv.org/pdf/2304.05974v2.pdf | Regularizing Contrastive Predictive Coding for Speech Applications | Self-supervised methods such as Contrastive predictive Coding (CPC) have greatly improved the quality of the unsupervised representations. These representations significantly reduce the amount of labeled data needed for downstream task performance, such as automatic speech recognition. CPC learns representations by lea... | ['Najim Dehak', 'Laureano Moro-Velazquez', 'Piotr Żelasko', 'Jesús Villalba', 'Saurabhchand Bhati'] | 2023-04-12 | null | null | null | null | ['acoustic-unit-discovery'] | ['speech'] | [ 4.38232213e-01 3.25195044e-01 -3.61945122e-01 -7.05470622e-01
-1.03282320e+00 -4.94654864e-01 7.65589297e-01 -3.42774380e-04
-5.27425826e-01 5.72843015e-01 5.55752873e-01 -5.71210027e-01
4.45441067e-01 -2.69898683e-01 -9.17355359e-01 -6.77616894e-01
-1.01374783e-01 2.98298329e-01 -6.55075014e-02 2.78146155... | [14.451542854309082, 6.532403469085693] |
fca12cf1-f039-4f28-819f-dab66a660476 | web-scale-photo-hash-clustering-on-a-single | null | null | http://openaccess.thecvf.com/content_cvpr_2015/html/Gong_Web_Scale_Photo_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Gong_Web_Scale_Photo_2015_CVPR_paper.pdf | Web Scale Photo Hash Clustering on A Single Machine | This paper addresses the problem of clustering a very large number of photos (i.e. hundreds of millions a day) in a stream into millions of clusters. This is particularly important as the popularity of photo sharing websites, such as Facebook, Google, and Instagram. Given large number of photos available online, how to... | ['Yunchao Gong', 'Marcin Pawlowski', 'Louis Brandy', 'Fei Yang', 'Rob Fergus', 'Lubomir Bourdev'] | 2015-06-01 | null | null | null | cvpr-2015-6 | ['online-clustering', 'spam-detection'] | ['computer-vision', 'natural-language-processing'] | [-1.39085859e-01 -2.96373636e-01 -2.17949212e-01 -2.53110021e-01
-6.11717880e-01 -9.13291276e-01 4.71680641e-01 8.07499766e-01
-4.89381015e-01 3.73878516e-02 -7.43143782e-02 8.90398175e-02
1.65440962e-02 -1.19581902e+00 -7.44425356e-01 -6.23701930e-01
-4.16174471e-01 6.06002510e-01 6.44389331e-01 2.52714157... | [7.44149112701416, 4.730705738067627] |
4b5c5ba6-72a5-4525-999f-9f2823126576 | a-benchmark-for-automatic-medical | 2204.08997 | null | https://arxiv.org/abs/2204.08997v3 | https://arxiv.org/pdf/2204.08997v3.pdf | A Benchmark for Automatic Medical Consultation System: Frameworks, Tasks and Datasets | In recent years, interest has arisen in using machine learning to improve the efficiency of automatic medical consultation and enhance patient experience. In this article, we propose two frameworks to support automatic medical consultation, namely doctor-patient dialogue understanding and task-oriented interaction. We ... | ['Jiajie Peng', 'Zhongyu Wei', 'Xuanjing Huang', 'Qi Zhang', 'Jianye Hao', 'Cheng Zhong', 'Qianyuan Yao', 'Hongyi Fang', 'Zhiwei Li', 'Wei Chen'] | 2022-04-19 | null | null | null | null | ['medical-report-generation', 'dialogue-understanding', 'dialogue-act-classification'] | ['medical', 'natural-language-processing', 'natural-language-processing'] | [ 2.55786479e-01 1.05359542e+00 -3.98473531e-01 -7.55730808e-01
-1.07972550e+00 -5.57318747e-01 5.91525137e-01 4.79928881e-01
-3.22381020e-01 1.14428735e+00 8.32834899e-01 -4.55164075e-01
-6.49692118e-02 -2.37175286e-01 3.58695328e-01 -3.86871278e-01
1.94118649e-01 1.08980596e+00 -1.80004552e-01 -1.33704841... | [12.408304214477539, 8.37809944152832] |
b4f40ab3-29ab-438e-afe5-4aa542192337 | chain-based-discriminative-autoencoders-for | 2203.13687 | null | https://arxiv.org/abs/2203.13687v3 | https://arxiv.org/pdf/2203.13687v3.pdf | Chain-based Discriminative Autoencoders for Speech Recognition | In our previous work, we proposed a discriminative autoencoder (DcAE) for speech recognition. DcAE combines two training schemes into one. First, since DcAE aims to learn encoder-decoder mappings, the squared error between the reconstructed speech and the input speech is minimized. Second, in the code layer, frame-base... | ['Hsin-Min Wang', 'Yao-Fei Cheng', 'Pin-Tuan Huang', 'Hung-Shin Lee'] | 2022-03-25 | null | null | null | null | ['robust-speech-recognition'] | ['speech'] | [ 1.07504338e-01 5.97608760e-02 3.51943135e-01 -6.35348618e-01
-1.09020698e+00 -3.19783390e-01 5.53764999e-01 -3.67641926e-01
-3.13485861e-01 3.90131444e-01 3.97847146e-01 -3.09511513e-01
2.21153826e-01 -4.96607602e-01 -5.43537796e-01 -8.06377590e-01
7.26486072e-02 1.93679165e-02 -6.78484365e-02 1.35075852... | [14.797351837158203, 6.400570392608643] |
5d05fa79-42cc-423f-881b-b456ca1ecc11 | simultaneous-iris-and-periocular-region | 1908.00069 | null | https://arxiv.org/abs/1908.00069v1 | https://arxiv.org/pdf/1908.00069v1.pdf | Simultaneous Iris and Periocular Region Detection Using Coarse Annotations | In this work, we propose to detect the iris and periocular regions simultaneously using coarse annotations and two well-known object detectors: YOLOv2 and Faster R-CNN. We believe coarse annotations can be used in recognition systems based on the iris and periocular regions, given the much smaller engineering effort re... | ['Diego R. Lucio', 'Rayson Laroca', 'Gladston Moreira', 'Luiz A. Zanlorensi', 'David Menotti'] | 2019-07-31 | null | null | null | null | ['iris-segmentation'] | ['medical'] | [ 9.76007134e-02 2.58813322e-01 -1.33084401e-01 -1.65472731e-01
-5.53533733e-01 -5.94775200e-01 3.61012995e-01 1.61024798e-02
-4.62196708e-01 1.97674543e-01 -1.35549977e-01 -1.46685034e-01
-1.42970890e-01 -3.69967580e-01 -3.51003855e-01 -7.69578695e-01
1.35170072e-01 2.88075387e-01 -1.30795255e-01 1.19223885... | [3.743572235107422, -3.631558895111084] |
1906437b-de2a-4436-9edc-6f9a4fb6faf4 | skinnet-a-deep-learning-framework-for-skin | 1806.09522 | null | http://arxiv.org/abs/1806.09522v1 | http://arxiv.org/pdf/1806.09522v1.pdf | SkinNet: A Deep Learning Framework for Skin Lesion Segmentation | There has been a steady increase in the incidence of skin cancer worldwide,
with a high rate of mortality. Early detection and segmentation of skin lesions
are crucial for timely diagnosis and treatment, necessary to improve the
survival rate of patients. However, skin lesion segmentation is a challenging
task due to t... | ['Sulaiman Vesal', 'Nishant Ravikumar', 'Andreas Maier'] | 2018-06-25 | null | null | null | null | ['skin-lesion-segmentation'] | ['medical'] | [ 3.02753478e-01 -2.51480252e-01 -2.88602799e-01 8.11423436e-02
-6.73008442e-01 -4.83919322e-01 5.42303860e-01 4.95555013e-01
-6.73566878e-01 8.23063076e-01 3.56711377e-03 2.79462188e-02
-1.94134101e-01 -7.04133332e-01 -8.37151557e-02 -9.68391359e-01
6.34065345e-02 4.24599051e-02 3.89209211e-01 8.44804421... | [15.630043983459473, -2.9466097354888916] |
e385fdca-417e-43b1-9fa2-71595cc5ab4b | unifying-bayesian-inference-and-vector-space | null | null | https://aclanthology.org/P15-1081 | https://aclanthology.org/P15-1081.pdf | Unifying Bayesian Inference and Vector Space Models for Improved Decipherment | null | ['Qing Dou', 'Chris Dyer', 'Ashish Vaswani', 'Kevin Knight'] | 2015-07-01 | unifying-bayesian-inference-and-vector-space-1 | https://aclanthology.org/P15-1081 | https://aclanthology.org/P15-1081.pdf | ijcnlp-2015-7 | ['decipherment'] | ['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... | [-7.327815532684326, 3.644780158996582] |
2df71a64-08b5-4190-a280-aded6c857936 | sars-cov-2-s-closest-relative-ratg13-was | 2111.09469 | null | https://arxiv.org/abs/2111.09469v2 | https://arxiv.org/pdf/2111.09469v2.pdf | SARS-CoV-2's closest relative, RaTG13, was generated from a bat transcriptome not a fecal swab: implications for the origin of COVID-19 | RaTG13 is the closest related coronavirus genome phylogenetically to SARS-CoV-2, consequently understanding its provenance is of key importance to understanding the origin of the COVID-19 pandemic. The RaTG13 NGS dataset is attributed to a fecal swab from the intermediate horseshoe bat Rhinolophus affinis. However, seq... | ['Steven E Massey'] | 2021-11-18 | null | null | null | null | ['virology'] | ['miscellaneous'] | [ 7.33312309e-01 -4.26030725e-01 3.70296091e-02 7.12875463e-03
-3.71159226e-01 -1.03039563e+00 3.71573597e-01 2.91549712e-01
-3.01894277e-01 8.42054963e-01 3.58163387e-01 -3.74094397e-01
2.73268223e-01 -6.42746449e-01 -8.49068999e-01 -1.06265461e+00
-1.87753752e-01 8.86990666e-01 -5.38030148e-01 -1.56383649... | [4.858774662017822, 5.081958293914795] |
ad74bd19-f17d-489a-bbb4-02d8a083e689 | when-sam-meets-shadow-detection | 2305.11513 | null | https://arxiv.org/abs/2305.11513v1 | https://arxiv.org/pdf/2305.11513v1.pdf | When SAM Meets Shadow Detection | As a promptable generic object segmentation model, segment anything model (SAM) has recently attracted significant attention, and also demonstrates its powerful performance. Nevertheless, it still meets its Waterloo when encountering several tasks, e.g., medical image segmentation, camouflaged object detection, etc. In... | ['HUI ZHANG', 'Leiping Jie'] | 2023-05-19 | null | null | null | null | ['shadow-detection'] | ['computer-vision'] | [ 3.19440722e-01 -1.14358000e-01 -2.90174961e-01 -2.18299165e-01
-5.68965256e-01 -2.75469691e-01 2.89374739e-01 -1.17531635e-01
-3.97732943e-01 7.15575993e-01 -2.73513824e-01 -3.65406334e-01
3.82534236e-01 -1.59892425e-01 -2.09170848e-01 -9.53614950e-01
2.36695305e-01 1.36588678e-01 9.30096507e-01 3.84856462... | [9.642765045166016, -0.18088886141777039] |
41d2cf87-4228-4865-8955-adaefd3b91ca | independent-prototype-propagation-for-zero | 2106.00305 | null | https://arxiv.org/abs/2106.00305v2 | https://arxiv.org/pdf/2106.00305v2.pdf | Independent Prototype Propagation for Zero-Shot Compositionality | Humans are good at compositional zero-shot reasoning; someone who has never seen a zebra before could nevertheless recognize one when we tell them it looks like a horse with black and white stripes. Machine learning systems, on the other hand, usually leverage spurious correlations in the training data, and while such ... | ['Gertjan Burghouts', 'Doina Bucur', 'Frank Ruis'] | 2021-06-01 | null | http://proceedings.neurips.cc/paper/2021/hash/584b98aac2dddf59ee2cf19ca4ccb75e-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/584b98aac2dddf59ee2cf19ca4ccb75e-Paper.pdf | neurips-2021-12 | ['compositional-zero-shot-learning'] | ['computer-vision'] | [ 3.92454296e-01 1.52568623e-01 -1.21707052e-01 -5.23937762e-01
-2.54394621e-01 -6.03388011e-01 7.88805902e-01 3.08960319e-01
-3.31582844e-01 5.39858401e-01 -1.99887902e-03 2.50993297e-03
-3.17038178e-01 -9.40784335e-01 -8.91734004e-01 -6.74414098e-01
1.26569793e-02 9.80179191e-01 5.07245660e-01 -3.93502891... | [10.250089645385742, 2.303956985473633] |
7740d8eb-0ff9-4d6c-8510-08baab61bb78 | smaragd-synthesized-smatch-for-accurate-and | 2203.13226 | null | https://arxiv.org/abs/2203.13226v2 | https://arxiv.org/pdf/2203.13226v2.pdf | SMARAGD: Learning SMatch for Accurate and Rapid Approximate Graph Distance | The similarity of graph structures, such as Meaning Representations (MRs), is often assessed via structural matching algorithms, such as Smatch (Cai and Knight, 2013). However, Smatch involves a combinatorial problem that suffers from NP-completeness, making large-scale applications, e.g., graph clustering or search, i... | ['Anette Frank', 'Philipp Meier', 'Juri Opitz'] | 2022-03-24 | null | null | null | null | ['graph-clustering'] | ['graphs'] | [ 5.42026162e-01 4.64016527e-01 -3.99182320e-01 -2.77546167e-01
-1.01598978e+00 -9.52691376e-01 2.06004784e-01 5.71604013e-01
-1.97696090e-01 5.62764704e-01 1.53632417e-01 -5.23201108e-01
-3.33434999e-01 -1.00520778e+00 -9.79790866e-01 -1.99729413e-01
-9.70950872e-02 8.34851801e-01 -2.24017084e-01 2.01909654... | [7.098536968231201, 6.190821647644043] |
fbd1f793-aeb3-465d-805f-9c2ed1a0eaa4 | uet-headpose-a-sensor-based-top-view-head | 2111.07039 | null | https://arxiv.org/abs/2111.07039v1 | https://arxiv.org/pdf/2111.07039v1.pdf | UET-Headpose: A sensor-based top-view head pose dataset | Head pose estimation is a challenging task that aims to solve problems related to predicting three dimensions vector, that serves for many applications in human-robot interaction or customer behavior. Previous researches have proposed some precise methods for collecting head pose data. But those methods require either ... | ['Long Tran Quoc', 'Duc Tran Minh', 'Hoang Nguyen Viet', 'Tuan Nguyen Dinh', 'Linh Nguyen Viet'] | 2021-11-13 | null | null | null | null | ['head-pose-estimation'] | ['computer-vision'] | [-5.90142429e-01 -1.13576509e-01 1.23565741e-01 -6.71482027e-01
-5.93920410e-01 -1.57481492e-01 1.62052959e-01 -2.27108851e-01
-5.54987609e-01 5.32841384e-01 3.56885642e-01 2.46181190e-01
-9.55966581e-03 -4.40813512e-01 -5.55605531e-01 -6.11747622e-01
3.40701416e-02 7.04917371e-01 1.98002532e-01 -3.75539869... | [13.801108360290527, 0.2518424689769745] |
606e20ef-5177-4a3d-82cd-042f17b8cc9e | fully-unsupervised-training-of-few-shot | 2210.02732 | null | https://arxiv.org/abs/2210.02732v2 | https://arxiv.org/pdf/2210.02732v2.pdf | Fully Unsupervised Training of Few-shot Keyword Spotting | For training a few-shot keyword spotting (FS-KWS) model, a large labeled dataset containing massive target keywords has known to be essential to generalize to arbitrary target keywords with only a few enrollment samples. To alleviate the expensive data collection with labeling, in this paper, we propose a novel FS-KWS ... | ['Nam Soo Kim', 'Min Hyun Han', 'Sung Hwan Mun', 'Minchan Kim', 'Dongjune Lee'] | 2022-10-06 | null | null | null | null | ['keyword-spotting'] | ['speech'] | [ 3.47197235e-01 1.50550306e-01 -2.49288812e-01 -5.17263234e-01
-1.16064608e+00 -5.94378412e-01 7.16292560e-01 -2.61845943e-02
-4.23914462e-01 7.80327082e-01 -7.70868585e-02 -1.84335992e-01
5.98506033e-02 -5.75788498e-01 -7.29740202e-01 -4.80185181e-01
5.24412692e-01 4.84228939e-01 2.50186056e-01 -1.34894669... | [14.096206665039062, 6.421380996704102] |
e63963e8-2565-4aca-adb6-03c74b697c1e | learning-to-prune-instances-of-steiner-tree | 2208.11985 | null | https://arxiv.org/abs/2208.11985v2 | https://arxiv.org/pdf/2208.11985v2.pdf | Learning to Prune Instances of Steiner Tree Problem in Graphs | We consider the Steiner tree problem on graphs where we are given a set of nodes and the goal is to find a tree sub-graph of minimum weight that contains all nodes in the given set, potentially including additional nodes. This is a classical NP-hard combinatorial optimisation problem. In recent years, a machine learnin... | ['Deepak Ajwani', 'Jiwei Zhang'] | 2022-08-25 | null | null | null | null | ['steiner-tree-problem'] | ['graphs'] | [ 6.88212454e-01 7.83915579e-01 -6.07765675e-01 -1.21217221e-01
-6.45603836e-01 -6.49144292e-01 1.11141624e-02 4.21213746e-01
-3.72125232e-03 7.56517828e-01 -5.06195188e-01 -5.45728326e-01
-7.15000868e-01 -1.00988090e+00 -6.25692010e-01 -5.85195601e-01
-7.16702461e-01 9.72511411e-01 2.11242199e-01 -4.21544164... | [5.241411209106445, 3.025007724761963] |
b4bbcec4-790a-4580-ab9e-1622539d083e | divide-and-denoise-learning-from-noisy-labels-1 | null | null | https://aclanthology.org/2022.acl-long.141 | https://aclanthology.org/2022.acl-long.141.pdf | Divide and Denoise: Learning from Noisy Labels in Fine-Grained Entity Typing with Cluster-Wise Loss Correction | Fine-grained Entity Typing (FET) has made great progress based on distant supervision but still suffers from label noise. Existing FET noise learning methods rely on prediction distributions in an instance-independent manner, which causes the problem of confirmation bias. In this work, we propose a clustering-based los... | ['Ting Wang', 'Jie zhou', 'Haoyu Zhang', 'Kunyuan Pang'] | null | null | null | null | acl-2022-5 | ['entity-typing'] | ['natural-language-processing'] | [-1.56960618e-02 -2.31699586e-01 -3.08788866e-01 -6.99950576e-01
-1.31504869e+00 -2.41499797e-01 4.47302401e-01 1.99519843e-01
-6.87975109e-01 9.14040148e-01 1.38877451e-01 -7.37584308e-02
7.50092268e-02 -6.19040489e-01 -7.32122660e-01 -7.81364024e-01
2.54225284e-01 4.13040072e-01 3.36326927e-01 1.86435327... | [9.54353141784668, 8.844675064086914] |
4db446fa-b833-457f-a6fb-940718306460 | llmzip-lossless-text-compression-using-large | 2306.0405 | null | https://arxiv.org/abs/2306.04050v2 | https://arxiv.org/pdf/2306.04050v2.pdf | LLMZip: Lossless Text Compression using Large Language Models | We provide new estimates of an asymptotic upper bound on the entropy of English using the large language model LLaMA-7B as a predictor for the next token given a window of past tokens. This estimate is significantly smaller than currently available estimates in \cite{cover1978convergent}, \cite{lutati2023focus}. A natu... | ['Srinivas Shakkottai', 'Jean-Francois Chamberland', 'Dileep Kalathil', 'Krishna Narayanan', 'Chandra Shekhara Kaushik Valmeekam'] | 2023-06-06 | null | null | null | null | ['text-compression'] | ['natural-language-processing'] | [ 5.62123023e-02 1.54385373e-01 -3.74895513e-01 -1.31473914e-01
-1.11951518e+00 -1.85271144e-01 5.94277382e-01 6.26255333e-01
-9.41662371e-01 1.21103227e+00 5.41216493e-01 -6.91525280e-01
-3.13868700e-03 -8.87941241e-01 -8.17990005e-01 -2.53054231e-01
-4.01096523e-01 4.01164711e-01 3.60434979e-01 -3.51942718... | [8.539252281188965, 3.467489004135132] |
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