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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
36510044-1e43-4445-82f5-9ae82065e708 | exploiting-temporal-context-for-3d-human-pose | 1905.04266 | null | https://arxiv.org/abs/1905.04266v1 | https://arxiv.org/pdf/1905.04266v1.pdf | Exploiting temporal context for 3D human pose estimation in the wild | We present a bundle-adjustment-based algorithm for recovering accurate 3D human pose and meshes from monocular videos. Unlike previous algorithms which operate on single frames, we show that reconstructing a person over an entire sequence gives extra constraints that can resolve ambiguities. This is because videos ofte... | ['Carl Doersch', 'Anurag Arnab', 'Andrew Zisserman'] | 2019-05-10 | exploiting-temporal-context-for-3d-human-pose-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Arnab_Exploiting_Temporal_Context_for_3D_Human_Pose_Estimation_in_the_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Arnab_Exploiting_Temporal_Context_for_3D_Human_Pose_Estimation_in_the_CVPR_2019_paper.pdf | cvpr-2019-6 | ['monocular-3d-human-pose-estimation'] | ['computer-vision'] | [-2.00406373e-01 -1.90316401e-02 2.20716849e-01 -3.98944676e-01
-7.21031070e-01 -7.42524862e-01 3.74207020e-01 -4.04840052e-01
-6.67984426e-01 6.40949965e-01 6.12074852e-01 6.41915143e-01
4.18854713e-01 -1.69468388e-01 -9.60814297e-01 -1.11274123e-01
-2.52473623e-01 8.80348742e-01 3.47853124e-01 -2.53257930... | [7.090700149536133, -0.8899604082107544] |
998141e6-66c2-48d4-b547-eeef614bbe41 | we-never-go-out-of-style-motion | 2306.00559 | null | https://arxiv.org/abs/2306.00559v1 | https://arxiv.org/pdf/2306.00559v1.pdf | We never go out of Style: Motion Disentanglement by Subspace Decomposition of Latent Space | Real-world objects perform complex motions that involve multiple independent motion components. For example, while talking, a person continuously changes their expressions, head, and body pose. In this work, we propose a novel method to decompose motion in videos by using a pretrained image GAN model. We discover disen... | ['R. Venkatesh Babu', 'Piyush Tiwari', 'Raghav Magazine', 'Rishubh Parihar'] | 2023-06-01 | null | null | null | null | ['motion-disentanglement', 'disentanglement'] | ['computer-vision', 'methodology'] | [ 6.93865195e-02 1.54918060e-01 -1.93605751e-01 -3.45010847e-01
-5.83151519e-01 -8.36436749e-01 6.33886039e-01 -1.11908233e+00
-1.14281476e-01 8.08600485e-01 4.88894522e-01 2.31911182e-01
3.61466318e-01 -3.77954870e-01 -9.98776197e-01 -9.47360218e-01
4.38837618e-01 4.09120262e-01 -5.15613675e-01 -2.81136613... | [10.980628967285156, -0.660194993019104] |
c3add9ba-0f50-4a8a-8acd-99cec7f8f3ce | automatic-text-summarization-methods-a | 2204.01849 | null | https://arxiv.org/abs/2204.01849v1 | https://arxiv.org/pdf/2204.01849v1.pdf | Automatic Text Summarization Methods: A Comprehensive Review | One of the most pressing issues that have arisen due to the rapid growth of the Internet is known as information overloading. Simplifying the relevant information in the form of a summary will assist many people because the material on any topic is plentiful on the Internet. Manually summarising massive amounts of text... | ['Arun Kumar Yadav', 'Jalpa Desai', 'Divakar Yadav'] | 2022-03-03 | null | null | null | null | ['art-analysis'] | ['computer-vision'] | [ 2.44756326e-01 2.07355604e-01 -3.07775319e-01 -2.62004565e-02
-5.85156679e-01 -5.41247487e-01 5.99027157e-01 1.05233240e+00
-3.78217757e-01 1.09470809e+00 9.78035212e-01 4.01171297e-02
-2.17712983e-01 -5.39599836e-01 -6.92827255e-02 -1.24471344e-01
2.96309233e-01 3.05309325e-01 2.44773492e-01 -4.33941901... | [12.435808181762695, 9.534560203552246] |
93ed24b8-472a-4a24-95de-a9caf0e97962 | infrared-and-visible-image-fusion-using-a-1 | 1811.02291 | null | https://arxiv.org/abs/1811.02291v5 | https://arxiv.org/pdf/1811.02291v5.pdf | MDLatLRR: A novel decomposition method for infrared and visible image fusion | Image decomposition is crucial for many image processing tasks, as it allows to extract salient features from source images. A good image decomposition method could lead to a better performance, especially in image fusion tasks. We propose a multi-level image decomposition method based on latent low-rank representation... | ['Xiao-Jun Wu', 'Josef Kittler', 'Hui Li'] | 2018-11-06 | null | null | null | null | ['infrared-and-visible-image-fusion'] | ['computer-vision'] | [ 4.07493472e-01 -6.42322540e-01 7.96415657e-02 -5.97852357e-02
-1.02322817e+00 4.67532920e-03 3.78469706e-01 8.44258443e-02
-2.83928573e-01 5.94899595e-01 6.07819378e-01 1.69343933e-01
-9.84908715e-02 -6.34001315e-01 -1.48477167e-01 -1.14338517e+00
3.63261014e-01 -4.88828748e-01 1.03199989e-01 -3.28646988... | [10.589327812194824, -1.904952049255371] |
35021838-071b-414b-bc10-54da7be9ab4f | dynamics-of-order-positions-and-related | 1505.04810 | null | http://arxiv.org/abs/1505.04810v2 | http://arxiv.org/pdf/1505.04810v2.pdf | Dynamics of Order Positions and Related Queues in a Limit Order Book | Order positions are key variables in algorithmic trading. This paper studies
the limiting behavior of order positions and related queues in a limit order
book. In addition to the fluid and diffusion limits for the processes,
fluctuations of order positions and related queues around their fluid limits
are analyzed. As a... | [] | 2015-10-13 | null | null | null | null | ['algorithmic-trading'] | ['time-series'] | [-8.61231267e-01 -4.62266326e-01 1.27714381e-01 -1.11545287e-02
1.33236155e-01 -1.12429118e+00 7.36120343e-01 1.83530405e-01
-5.82619190e-01 1.06618369e+00 -3.47548336e-01 -3.38070571e-01
-5.61302185e-01 -7.25168884e-01 -2.89258122e-01 -6.51828945e-01
-6.26239419e-01 1.01710331e+00 5.16248345e-01 -3.58824372... | [4.8436994552612305, 3.987200975418091] |
9f88f5c3-d12b-4401-a192-2231e6797768 | spatially-resolved-gene-expression-prediction | 2306.01859 | null | https://arxiv.org/abs/2306.01859v1 | https://arxiv.org/pdf/2306.01859v1.pdf | Spatially Resolved Gene Expression Prediction from H&E Histology Images via Bi-modal Contrastive Learning | Histology imaging is an important tool in medical diagnosis and research, enabling the examination of tissue structure and composition at the microscopic level. Understanding the underlying molecular mechanisms of tissue architecture is critical in uncovering disease mechanisms and developing effective treatments. Gene... | ['Bo wang', 'Gary D. Bader', 'Kuan Pang', 'Ronald Xie'] | 2023-06-02 | null | null | null | null | ['medical-diagnosis'] | ['medical'] | [ 6.57741129e-02 -5.94946980e-01 -9.62904096e-02 -1.33777469e-01
-1.00046933e+00 -7.14767933e-01 2.14578155e-02 4.64817137e-01
-3.24999303e-01 4.52147365e-01 1.37703672e-01 -9.79607180e-02
-1.83070540e-01 -7.39536107e-01 -9.87304822e-02 -1.62650752e+00
-4.06967372e-01 2.44140461e-01 -3.63997787e-01 1.93533689... | [15.10315990447998, -2.9880263805389404] |
054f0f5c-409e-473c-bc0d-bf22bb3742bd | joint-graph-learning-and-model-fitting-in | 2305.02573 | null | https://arxiv.org/abs/2305.02573v1 | https://arxiv.org/pdf/2305.02573v1.pdf | Joint Graph Learning and Model Fitting in Laplacian Regularized Stratified Models | Laplacian regularized stratified models (LRSM) are models that utilize the explicit or implicit network structure of the sub-problems as defined by the categorical features called strata (e.g., age, region, time, forecast horizon, etc.), and draw upon data from neighboring strata to enhance the parameter learning of ea... | ['Stephen Boyd', 'Akshay Agrawal', 'Junzi Zhang', 'Ziheng Cheng'] | 2023-05-04 | null | null | null | null | ['graph-clustering'] | ['graphs'] | [ 1.24707311e-01 3.63701612e-01 -4.06814575e-01 -2.54008621e-01
-6.52170420e-01 -3.99875820e-01 5.59283197e-01 3.78986180e-01
-2.32896823e-02 6.15474880e-01 1.16845869e-01 -2.91115552e-01
-8.28709126e-01 -7.33451366e-01 -5.09507298e-01 -9.17581439e-01
-5.31853199e-01 4.26198989e-01 9.53900665e-02 3.96843627... | [7.111222743988037, 4.891982555389404] |
0453ccc1-0d80-4e1d-adf5-f1540f89654f | learning-word-embeddings-efficiently-with | null | null | http://papers.nips.cc/paper/5165-learning-word-embeddings-efficiently-with-noise-contrastive-estimation | http://papers.nips.cc/paper/5165-learning-word-embeddings-efficiently-with-noise-contrastive-estimation.pdf | Learning word embeddings efficiently with noise-contrastive estimation | Continuous-valued word embeddings learned by neural language models have recently been shown to capture semantic and syntactic information about words very well, setting performance records on several word similarity tasks. The best results are obtained by learning high-dimensional embeddings from very large quantitie... | ['Andriy Mnih', 'Koray Kavukcuoglu'] | 2013-12-01 | null | null | null | neurips-2013-12 | ['learning-word-embeddings'] | ['methodology'] | [-4.10449743e-01 -1.17885925e-01 -2.34752968e-01 -2.74219155e-01
-7.74539530e-01 -5.54121077e-01 8.82279992e-01 7.26465583e-01
-1.13081324e+00 5.08342385e-01 3.25563729e-01 -3.69124025e-01
-1.04167253e-01 -8.01270485e-01 -5.53121984e-01 -5.83011210e-01
-2.38332212e-01 7.79690683e-01 3.05359453e-01 -3.13063890... | [10.491302490234375, 8.702796936035156] |
35daddd5-da66-416b-9728-b26d24b0390a | architectural-vision-for-quantum-computing-in | 2305.05238 | null | https://arxiv.org/abs/2305.05238v1 | https://arxiv.org/pdf/2305.05238v1.pdf | Architectural Vision for Quantum Computing in the Edge-Cloud Continuum | Quantum processing units (QPUs) are currently exclusively available from cloud vendors. However, with recent advancements, hosting QPUs is soon possible everywhere. Existing work has yet to draw from research in edge computing to explore systems exploiting mobile QPUs, or how hybrid applications can benefit from distri... | ['Felix Truger', 'Philipp Raith', 'Frank Leymann', 'Schahram Dustdar', 'Marvin Bechtold', 'Johanna Barzen', 'Alireza Furutanpey'] | 2023-05-09 | null | null | null | null | ['edge-computing'] | ['time-series'] | [-4.35360484e-02 -9.31433886e-02 -1.46079838e-01 -9.81535837e-02
-7.23709166e-01 -4.40085709e-01 5.76489195e-02 -1.66220576e-01
-1.96747348e-01 6.59901023e-01 -2.59238660e-01 -6.18110478e-01
-2.26686239e-01 -1.15518689e+00 -6.18540943e-01 -5.33633709e-01
-5.10022417e-02 4.86470342e-01 1.21010557e-01 -4.88205910... | [5.587704181671143, 4.93940544128418] |
e4ecaf73-73a3-43fd-8a96-13084f720f65 | a-survey-of-unsupervised-dependency-parsing | 2010.01535 | null | https://arxiv.org/abs/2010.01535v1 | https://arxiv.org/pdf/2010.01535v1.pdf | A Survey of Unsupervised Dependency Parsing | Syntactic dependency parsing is an important task in natural language processing. Unsupervised dependency parsing aims to learn a dependency parser from sentences that have no annotation of their correct parse trees. Despite its difficulty, unsupervised parsing is an interesting research direction because of its capabi... | ['Kewei Tu', 'Hwee Tou Ng', 'Yong Jiang', 'Wenjuan Han'] | 2020-10-04 | null | https://aclanthology.org/2020.coling-main.227 | https://aclanthology.org/2020.coling-main.227.pdf | coling-2020-8 | ['unsupervised-dependency-parsing'] | ['natural-language-processing'] | [-1.39758978e-02 5.34014642e-01 -5.66971838e-01 -1.04520786e+00
-7.55172551e-01 -8.01784694e-01 9.57247764e-02 3.52159381e-01
-3.19516748e-01 8.81237984e-01 5.21859050e-01 -6.78052485e-01
3.99477214e-01 -6.93165064e-01 -2.80152380e-01 -4.02402490e-01
-2.33948022e-01 3.66664469e-01 3.34125161e-01 -1.32539377... | [10.368191719055176, 9.72996711730957] |
e4bc04e6-801d-4ca9-8ebb-a64890b35f9f | practical-bandits-an-industry-perspective | 2302.01223 | null | https://arxiv.org/abs/2302.01223v1 | https://arxiv.org/pdf/2302.01223v1.pdf | Practical Bandits: An Industry Perspective | The bandit paradigm provides a unified modeling framework for problems that require decision-making under uncertainty. Because many business metrics can be viewed as rewards (a.k.a. utilities) that result from actions, bandit algorithms have seen a large and growing interest from industrial applications, such as search... | ['Devesh Parekh', 'Zahra Nazari', 'Ben London', 'Ying Li', 'Olivier Jeunen', 'Bram van den Akker'] | 2023-02-02 | null | null | null | null | ['decision-making-under-uncertainty', 'decision-making-under-uncertainty'] | ['medical', 'reasoning'] | [ 1.65150434e-01 1.63571220e-02 -1.01861906e+00 -3.73955339e-01
-9.17850912e-01 -8.03050876e-01 4.88044620e-01 -1.19834982e-01
-1.17734514e-01 9.95513260e-01 9.12840888e-02 -7.83390760e-01
-9.30333257e-01 -6.13208532e-01 -5.71328819e-01 -7.89595366e-01
-1.20397501e-01 5.64923882e-01 -2.95657516e-01 -1.26488462... | [4.526704788208008, 3.2534019947052] |
0a295534-9c7d-46bc-94e9-6ef7090f067d | odianlps-participation-in-wat2020 | null | null | https://aclanthology.org/2020.wat-1.10 | https://aclanthology.org/2020.wat-1.10.pdf | ODIANLP’s Participation in WAT2020 | This paper describes the ODIANLP submission to WAT 2020. We have participated in the English-Hindi Multimodal task and Indic task. We have used the state-of-the-art Transformer model for the translation task and InceptionResNetV2 for the Hindi Image Captioning task. Our submission tops in English->Hindi Multimodal task... | ['Ondřej Bojar', 'Biranchi Narayan Nayak', 'Priyanka Pattnaik', 'Satya Prakash Biswal', 'Debasish Kumar Mallick', 'Satya Ranjan Dash', 'Amulya Ratna Dash', 'Petr Motlicek', 'Shantipriya Parida'] | null | null | null | null | aacl-wat-2020-12 | ['hindi-image-captioning'] | ['computer-vision'] | [-3.32389235e-01 1.33047014e-01 -1.22405902e-01 -4.96942073e-01
-1.62001622e+00 -7.28931189e-01 1.10884583e+00 -5.21858573e-01
-7.73435712e-01 1.21848786e+00 4.61224616e-01 -4.44293916e-01
4.82012689e-01 -2.10729420e-01 -9.60428178e-01 -1.16327748e-01
3.49402219e-01 1.32912445e+00 1.69593483e-01 -6.95574224... | [11.438986778259277, 1.5291764736175537] |
6f606846-c92d-4ccb-850f-76fb54aaab76 | efficient-belief-space-planning-in-high | 2112.14428 | null | https://arxiv.org/abs/2112.14428v1 | https://arxiv.org/pdf/2112.14428v1.pdf | Efficient Belief Space Planning in High-Dimensional State Spaces using PIVOT: Predictive Incremental Variable Ordering Tactic | In this work, we examine the problem of online decision making under uncertainty, which we formulate as planning in the belief space. Maintaining beliefs (i.e., distributions) over high-dimensional states (e.g., entire trajectories) was not only shown to significantly improve accuracy, but also allows planning with inf... | ['Vadim Indelman', 'Khen Elimelech'] | 2021-12-29 | null | null | null | null | ['decision-making-under-uncertainty', 'decision-making-under-uncertainty'] | ['medical', 'reasoning'] | [ 2.13863924e-01 4.49582219e-01 -2.24029481e-01 -1.87889993e-01
-6.22660697e-01 -5.94400227e-01 5.61644375e-01 7.82822430e-01
-6.96081102e-01 1.11592197e+00 6.91235736e-02 -3.98069322e-01
-4.16970342e-01 -1.04424894e+00 -7.74833560e-01 -6.99620485e-01
-4.68714595e-01 9.68724728e-01 4.74169284e-01 -7.89935514... | [4.804194450378418, 1.8388780355453491] |
a423c621-6c29-4ced-97a6-4a6d8964292c | covi-agentsim-an-agent-based-model-for | 2010.16004 | null | https://arxiv.org/abs/2010.16004v1 | https://arxiv.org/pdf/2010.16004v1.pdf | COVI-AgentSim: an Agent-based Model for Evaluating Methods of Digital Contact Tracing | The rapid global spread of COVID-19 has led to an unprecedented demand for effective methods to mitigate the spread of the disease, and various digital contact tracing (DCT) methods have emerged as a component of the solution. In order to make informed public health choices, there is a need for tools which allow evalua... | ['Yoshua Bengio', 'Eilif B. Muller', 'Joanna Merckx', 'Christopher Pal', 'Irina Rish', 'Jian Tang', 'Bernhard Schölkopf', 'Yang Zhang', 'Joumana Ghosn', 'David Buckeridge', 'Marc-Andre Rousseau', 'Satya Ortiz-Gagné', 'Pierre Luc Carrier', 'Gaétan Marceau Caron', 'Olexa Bilaniuk', 'Meng Qu', 'Akshay Patel', 'Andrew Will... | 2020-10-30 | null | https://openreview.net/forum?id=07iDTU-KFK | https://openreview.net/pdf?id=07iDTU-KFK | null | ['virology'] | ['miscellaneous'] | [ 2.08499402e-01 -3.21349889e-01 -2.14134455e-01 7.49932528e-02
-4.92632762e-02 -6.21659994e-01 1.00863874e+00 7.24472582e-01
-5.32596469e-01 7.61532724e-01 1.40964925e-01 -7.47641265e-01
-7.63374031e-01 -1.07176018e+00 -2.75519252e-01 -3.45671535e-01
-5.76161504e-01 7.62686133e-01 2.78149903e-01 -2.56133944... | [5.984170913696289, 4.387811660766602] |
c7e4ed0c-b3c7-43be-969d-58bb0840148b | alarm-based-prescriptive-process-monitoring | 1803.08706 | null | http://arxiv.org/abs/1803.08706v2 | http://arxiv.org/pdf/1803.08706v2.pdf | Alarm-Based Prescriptive Process Monitoring | Predictive process monitoring is concerned with the analysis of events
produced during the execution of a process in order to predict the future state
of ongoing cases thereof. Existing techniques in this field are able to
predict, at each step of a case, the likelihood that the case will end up in an
undesired outcome... | ['Fabrizio Maria Maggi', 'Niek Tax', 'Irene Teinemaa', 'Massimiliano de Leoni', 'Marlon Dumas'] | 2018-03-23 | null | null | null | null | ['predictive-process-monitoring'] | ['time-series'] | [ 9.06853735e-01 3.69671047e-01 2.64584959e-01 -3.25155556e-01
-2.05434442e-01 -2.18485191e-01 8.50453436e-01 1.21817148e+00
-9.74919349e-02 5.91601253e-01 8.88438374e-02 -4.39707190e-01
-6.07355773e-01 -1.10954571e+00 -2.87855476e-01 -3.65801871e-01
3.36424410e-02 6.18683696e-01 1.84781820e-01 5.08154094... | [8.587885856628418, 5.985487461090088] |
73c20861-c707-46f4-b179-846f9e2dd1c4 | motion-aware-memory-network-for-fast-video | 2208.00946 | null | https://arxiv.org/abs/2208.00946v1 | https://arxiv.org/pdf/2208.00946v1.pdf | Motion-aware Memory Network for Fast Video Salient Object Detection | Previous methods based on 3DCNN, convLSTM, or optical flow have achieved great success in video salient object detection (VSOD). However, they still suffer from high computational costs or poor quality of the generated saliency maps. To solve these problems, we design a space-time memory (STM)-based network, which extr... | ['Xiaofei He', 'Ronghua Liang', 'Dongdong Zhao', 'Guodao Sun', 'Peipei Li', 'Haoran Liang', 'Xing Zhao'] | 2022-08-01 | null | null | null | null | ['video-salient-object-detection'] | ['computer-vision'] | [ 1.70606777e-01 -4.01576012e-01 -5.62209725e-01 -2.02052444e-01
-3.21353674e-01 1.21355787e-01 2.46423945e-01 -2.23440438e-04
-4.32607502e-01 6.81442976e-01 3.18955719e-01 1.79150790e-01
-1.13165928e-02 -7.67683446e-01 -6.77946270e-01 -6.70703709e-01
9.85039398e-02 -3.31788123e-01 1.02275169e+00 5.31485640... | [9.507818222045898, -0.33586257696151733] |
19d93a14-18a9-43f8-824f-078e711fcfb4 | medical-code-prediction-with-multi-view | 1811.01468 | null | http://arxiv.org/abs/1811.01468v1 | http://arxiv.org/pdf/1811.01468v1.pdf | Medical code prediction with multi-view convolution and description-regularized label-dependent attention | A ubiquitous task in processing electronic medical data is the assignment of
standardized codes representing diagnoses and/or procedures to free-text
documents such as medical reports. This is a difficult natural language
processing task that requires parsing long, heterogeneous documents and
selecting a set of appropr... | ['David Suendermann-Oeft', 'Greg P. Finley', 'Slava Baryshnikov', 'Vignesh Murali', 'Najmeh Sadoughi', 'Mark Miller', 'Nico Axtmann', 'James Fone', 'Maxim Korenevski'] | 2018-11-05 | null | null | null | null | ['medical-code-prediction'] | ['medical'] | [ 3.49766284e-01 2.71958202e-01 -3.03598374e-01 -5.62928379e-01
-1.46121478e+00 -5.83997726e-01 2.04109833e-01 7.45232821e-01
-4.37452883e-01 5.70686042e-01 6.02569580e-01 -5.45780122e-01
-1.39438778e-01 -4.03690904e-01 -4.80453163e-01 -2.97671705e-01
-3.93141359e-01 7.34780014e-01 -4.25977379e-01 9.21246782... | [8.028382301330566, 6.861257553100586] |
42f415fc-d174-4a8a-9f0c-6473ca07d0ae | investigations-on-the-inference-optimization | 1911.12993 | null | https://arxiv.org/abs/1911.12993v1 | https://arxiv.org/pdf/1911.12993v1.pdf | Investigations on the inference optimization techniques and their impact on multiple hardware platforms for Semantic Segmentation | In this work, the task of pixel-wise semantic segmentation in the context of self-driving with a goal to reduce the inference time is explored. Fully Convolutional Network (FCN-8s, FCN-16s, and FCN-32s) with a VGG16 encoder architecture and skip connections is trained and validated on the Cityscapes dataset. Numerical ... | ['Sethu Hareesh Kolluru'] | 2019-11-29 | null | null | null | null | ['inference-optimization'] | ['audio'] | [ 4.99748848e-02 3.64404470e-01 -1.20516486e-01 -4.67298537e-01
-8.86670724e-02 -3.48298222e-01 3.07327062e-01 -3.06565940e-01
-8.07021081e-01 5.95797002e-01 -3.77802730e-01 -8.20771813e-01
-1.34803116e-01 -7.14727700e-01 -6.78443849e-01 -5.21867514e-01
5.87746166e-02 3.90439294e-03 2.75712967e-01 1.51125744... | [9.402623176574707, -0.2659558057785034] |
58a871c3-9dca-4466-8b9b-2e555ccb2e22 | cross-lingual-unsupervised-sentiment | null | null | https://aclanthology.org/2020.acl-main.510 | https://aclanthology.org/2020.acl-main.510.pdf | Cross-Lingual Unsupervised Sentiment Classification with Multi-View Transfer Learning | Recent neural network models have achieved impressive performance on sentiment classification in English as well as other languages. Their success heavily depends on the availability of a large amount of labeled data or parallel corpus. In this paper, we investigate an extreme scenario of cross-lingual sentiment classi... | ['Hongliang Fei', 'Ping Li'] | 2020-07-01 | null | null | null | acl-2020-6 | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [ 1.68113753e-01 -1.53631881e-01 -5.39276600e-01 -9.90053654e-01
-1.13326430e+00 -7.02984035e-01 5.60108662e-01 -3.96146685e-01
-4.73300874e-01 6.47256076e-01 2.99362659e-01 -5.34070432e-01
7.73864627e-01 -4.46846277e-01 -6.83722496e-01 -6.13667369e-01
7.07393587e-01 3.80822837e-01 -4.47298318e-01 -4.36961353... | [11.198049545288086, 9.76653003692627] |
0c413ff2-2308-408a-979a-c141505193de | k-way-p-spectral-clustering-on-grassmann | 2008.13210 | null | https://arxiv.org/abs/2008.13210v2 | https://arxiv.org/pdf/2008.13210v2.pdf | Multiway $p$-spectral graph cuts on Grassmann manifolds | Nonlinear reformulations of the spectral clustering method have gained a lot of recent attention due to their increased numerical benefits and their solid mathematical background. We present a novel direct multiway spectral clustering algorithm in the $p$-norm, for $p \in (1, 2]$. The problem of computing multiple eige... | ['Olaf Schenk', 'Christie Louis Alappat', 'Dimosthenis Pasadakis', 'Gerhard Wellein'] | 2020-08-30 | null | null | null | null | ['spectral-graph-clustering'] | ['graphs'] | [ 9.69480649e-02 9.95035619e-02 -1.43854216e-01 -1.98703453e-01
-4.52760011e-01 -4.00970817e-01 -3.13108088e-03 3.76223251e-02
-2.79213548e-01 3.85084867e-01 -3.49042028e-01 7.77897835e-02
-7.25614548e-01 -5.95921755e-01 -3.91697407e-01 -9.97199833e-01
-4.22388375e-01 3.78931344e-01 -9.13918018e-02 -3.00765969... | [7.499814510345459, 4.750634670257568] |
0a6e8e29-5cac-4f80-8ab6-070c9a26aa43 | sign-language-translation-from-instructional | 2304.06371 | null | https://arxiv.org/abs/2304.06371v2 | https://arxiv.org/pdf/2304.06371v2.pdf | Sign Language Translation from Instructional Videos | The advances in automatic sign language translation (SLT) to spoken languages have been mostly benchmarked with datasets of limited size and restricted domains. Our work advances the state of the art by providing the first baseline results on How2Sign, a large and broad dataset. We train a Transformer over I3D video fe... | ['Xavier Giró-i-Nieto', 'Jordi Torres', 'Amanda Duarte', 'Gerard I. Gállego', 'Laia Tarrés'] | 2023-04-13 | null | null | null | null | ['sign-language-translation'] | ['computer-vision'] | [ 9.69796479e-02 -3.32738638e-01 -5.56341588e-01 -3.81568193e-01
-1.39745152e+00 -7.25101531e-01 9.26714540e-01 -8.19232583e-01
-6.73595726e-01 9.09394264e-01 5.91548443e-01 -1.88694358e-01
4.30705220e-01 -1.23008996e-01 -7.53397346e-01 -4.52342361e-01
1.65524065e-01 6.25676215e-01 5.46933830e-01 -1.61645904... | [9.20506763458252, -6.533027172088623] |
80669735-b00c-4fff-b134-b8bd8eadb390 | data-augmentation-and-cnn-classification-for | 2108.07148 | null | https://arxiv.org/abs/2108.07148v2 | https://arxiv.org/pdf/2108.07148v2.pdf | Data Augmentation and CNN Classification For Automatic COVID-19 Diagnosis From CT-Scan Images On Small Dataset | We present an automatic COVID1-19 diagnosis framework from lung CT images. The focus is on signal processing and classification on small datasets with efforts putting into exploring data preparation and augmentation to improve the generalization capability of the 2D CNN classification models. We propose a unique and ef... | ['Hongwei Guo', 'Weijun Tan'] | 2021-08-16 | null | null | null | null | ['covid-19-detection'] | ['medical'] | [ 3.87671500e-01 1.50112540e-01 -1.54678211e-01 -2.14294299e-01
-5.62041104e-01 6.97300062e-02 1.88425079e-01 1.83374867e-01
-7.39950061e-01 4.44305509e-01 6.80009797e-02 -4.25126106e-01
5.91031387e-02 -8.92122328e-01 -1.76603243e-01 -7.81907976e-01
-8.73961598e-02 1.74964905e-01 5.57468235e-01 1.46578953... | [15.20095443725586, -2.1770846843719482] |
b72bec36-1731-4260-8c31-7e469979d2ca | underwater-image-filtering-methods-datasets | 2012.12258 | null | https://arxiv.org/abs/2012.12258v1 | https://arxiv.org/pdf/2012.12258v1.pdf | Underwater image filtering: methods, datasets and evaluation | Underwater images are degraded by the selective attenuation of light that distorts colours and reduces contrast. The degradation extent depends on the water type, the distance between an object and the camera, and the depth under the water surface the object is at. Underwater image filtering aims to restore or to enhan... | ['Andrea Cavallaro', 'Riccardo Mazzon', 'Chau Yi Li'] | 2020-12-22 | null | null | null | null | ['underwater-image-restoration'] | ['computer-vision'] | [ 5.68793476e-01 -3.89068514e-01 9.77016509e-01 -2.59436995e-01
-4.16005820e-01 -4.99820799e-01 4.32321280e-02 -1.13154380e-02
-8.43349397e-01 6.00715995e-01 4.85388339e-01 1.06767483e-01
-1.45280302e-01 -8.22405219e-01 -6.92130268e-01 -1.20172405e+00
-4.42020833e-01 -4.87898946e-01 1.47810534e-01 -3.97920132... | [10.695423126220703, -3.495652914047241] |
e4fc17bd-32ce-4d48-8b26-c07065e18d78 | pe-gan-prior-embedding-gan-for-pxd-images-at | 2303.00693 | null | https://arxiv.org/abs/2303.00693v1 | https://arxiv.org/pdf/2303.00693v1.pdf | PE-GAN: Prior Embedding GAN for PXD images at Belle II | The pixel vertex detector (PXD) is an essential part of the Belle II detector recording particle positions. Data from the PXD and other sensors allow us to reconstruct particle tracks and decay vertices. The effect of background hits on track reconstruction is simulated by adding measured or simulated background hit pa... | ['Matej srebre', 'Martin Ritter', 'Thomas Kuhr', 'Nikolai Hartmann', 'Hosein Hashemi'] | 2023-03-01 | null | null | null | null | ['conditional-image-generation'] | ['computer-vision'] | [ 2.76364058e-01 -3.57095420e-01 2.41799876e-01 -2.80395877e-02
-1.24482012e+00 -5.68068385e-01 8.62228036e-01 -1.58384934e-01
-5.10192811e-01 7.38661230e-01 2.20895130e-02 -2.29306325e-01
6.40731752e-01 -1.43909192e+00 -1.18292451e+00 -8.56072426e-01
3.71400863e-01 7.72448123e-01 6.51004553e-01 2.89897382... | [9.288627624511719, -3.5184519290924072] |
a05504da-6add-4dcb-a6c4-d300ed72094e | learning-opinion-dynamics-from-social-traces | 2006.01673 | null | https://arxiv.org/abs/2006.01673v1 | https://arxiv.org/pdf/2006.01673v1.pdf | Learning Opinion Dynamics From Social Traces | Opinion dynamics - the research field dealing with how people's opinions form and evolve in a social context - traditionally uses agent-based models to validate the implications of sociological theories. These models encode the causal mechanism that drives the opinion formation process, and have the advantage of being ... | ['Francesco Bonchi', 'Corrado Monti', 'Gianmarco De Francisci Morales'] | 2020-06-02 | null | null | null | null | ['link-sign-prediction'] | ['graphs'] | [-8.64328668e-02 3.04387987e-01 -5.53374738e-02 -2.20700249e-01
1.39927249e-02 -6.77838624e-01 1.15921986e+00 4.81776714e-01
-3.43611121e-01 7.93117762e-01 1.90285519e-01 -6.93107426e-01
-2.66514361e-01 -1.15836322e+00 -5.82100868e-01 -7.94429958e-01
-1.09355822e-01 8.30892146e-01 7.84624219e-02 -4.32338893... | [6.725534439086914, 5.081334590911865] |
7a563a18-4670-4314-9114-3799bbe2e487 | optimizing-software-effort-estimation-models | 1903.02079 | null | http://arxiv.org/abs/1903.02079v1 | http://arxiv.org/pdf/1903.02079v1.pdf | Optimizing Software Effort Estimation Models Using Firefly Algorithm | Software development effort estimation is considered a fundamental task for
software development life cycle as well as for managing project cost, time and
quality. Therefore, accurate estimation is a substantial factor in projects
success and reducing the risks. In recent years, software effort estimation has
received ... | ['Rizik M. H. Al-Sayyed', 'Hossam Faris', 'Ibrahim Aljarah', 'Nazeeh Ghatasheh'] | 2019-01-08 | null | null | null | null | ['metaheuristic-optimization'] | ['methodology'] | [-1.27097458e-01 -6.02071166e-01 6.28110319e-02 4.45500808e-03
-1.38144925e-01 -3.20010811e-01 -4.51492891e-03 6.13942623e-01
-4.31726038e-01 8.89172137e-01 -3.41209620e-01 1.48360461e-01
-6.71193540e-01 -9.78000700e-01 2.85264775e-02 -6.94314957e-01
4.14528877e-01 4.33535665e-01 -4.66761664e-02 -1.66023850... | [5.664723873138428, 3.4973769187927246] |
66b5101c-e641-4ce1-ba5f-ca5206a1113f | unsupervised-person-re-identification-with-2 | 2110.15610 | null | https://arxiv.org/abs/2110.15610v2 | https://arxiv.org/pdf/2110.15610v2.pdf | Unsupervised Person Re-Identification with Wireless Positioning under Weak Scene Labeling | Existing unsupervised person re-identification methods only rely on visual clues to match pedestrians under different cameras. Since visual data is essentially susceptible to occlusion, blur, clothing changes, etc., a promising solution is to introduce heterogeneous data to make up for the defect of visual data. Some w... | ['Houqiang Li', 'Qiaokang Xie', 'Wengang Zhou', 'Yiheng Liu'] | 2021-10-29 | null | null | null | null | ['scene-labeling', 'unsupervised-person-re-identification'] | ['computer-vision', 'computer-vision'] | [-9.91878808e-02 -2.44191036e-01 -1.56617090e-01 -3.23467463e-01
-2.55712986e-01 -5.97474337e-01 4.34121549e-01 1.88394859e-01
-5.02593875e-01 6.04414463e-01 1.45238012e-01 -5.56447217e-03
4.28640936e-03 -7.91097939e-01 -7.51934350e-01 -7.92069554e-01
1.07057728e-01 1.83126152e-01 1.54580325e-01 1.24038368... | [14.801668167114258, 1.0316684246063232] |
af42efab-375e-48eb-bc80-1c7e41cb954f | improving-implicit-feedback-based | 2305.05585 | null | https://arxiv.org/abs/2305.05585v1 | https://arxiv.org/pdf/2305.05585v1.pdf | Improving Implicit Feedback-Based Recommendation through Multi-Behavior Alignment | Recommender systems that learn from implicit feedback often use large volumes of a single type of implicit user feedback, such as clicks, to enhance the prediction of sparse target behavior such as purchases. Using multiple types of implicit user feedback for such target behavior prediction purposes is still an open qu... | ['Zhaochun Ren', 'Maarten de Rijke', 'Joemon Jose', 'Hengliang Luo', 'Xinlei Shi', 'Jiahuan Lei', 'Zhumin Chen', 'Pengjie Ren', 'Hanbing Wang', 'Xiangyuan Liu', 'Xin Xin'] | 2023-05-09 | null | null | null | null | ['open-question'] | ['natural-language-processing'] | [ 1.97317332e-01 -2.62862027e-01 -5.22413135e-01 -9.53360021e-01
-3.76884580e-01 -4.75497186e-01 3.74028653e-01 3.04221660e-02
-2.03333437e-01 4.78795201e-01 5.50770104e-01 -2.28900015e-01
-1.72444835e-01 -7.76067436e-01 -6.76502347e-01 -5.70046186e-01
1.35635752e-02 4.23436403e-01 -2.29819223e-01 -2.06381679... | [10.017494201660156, 5.567943572998047] |
6e3f994b-ff2a-4e4c-a9a7-106d3d58f1a8 | fully-digital-second-order-level-crossing | 2211.09848 | null | https://arxiv.org/abs/2211.09848v2 | https://arxiv.org/pdf/2211.09848v2.pdf | Fully Digital Second-order Level-crossing Sampling ADC for Data Saving in Sensing Sparse Signals | This paper presents a fully integrated second-order level-crossing sampling data converter for real-time data compression and feature extraction. Compared with level-sampling ADCs which sample at fixed voltage levels, the proposed circuits updates tracking thresholds using linear extrapolation, which forms a second-ord... | ['Wei Tang', 'Jaime Ramirez-Angulo', 'Xiaochen Tang', 'Mario Renteria-Pinon'] | 2022-11-17 | null | null | null | null | ['data-compression'] | ['time-series'] | [ 7.81729579e-01 -2.11388320e-01 -3.52342516e-01 -4.45175916e-01
-5.00824630e-01 -4.73253310e-01 5.63767105e-02 9.28754926e-01
-8.26860309e-01 5.90549946e-01 -1.69535950e-01 -2.22915024e-01
1.31506085e-01 -7.42400050e-01 -1.98768124e-01 -3.70046824e-01
-1.13329142e-01 -1.15259755e-02 6.11971855e-01 1.69940680... | [13.94454288482666, 3.1611549854278564] |
20e6bf2e-a9ac-4e5a-a9fa-c436e1291559 | tutoring-instruction-grounded-conversational | 2302.12623 | null | https://arxiv.org/abs/2302.12623v1 | https://arxiv.org/pdf/2302.12623v1.pdf | TUTORING: Instruction-Grounded Conversational Agent for Language Learners | In this paper, we propose Tutoring bot, a generative chatbot trained on a large scale of tutor-student conversations for English-language learning. To mimic a human tutor's behavior in language education, the tutor bot leverages diverse educational instructions and grounds to each instruction as additional input contex... | ['Jinyoung Yeo', 'Junmyung Lee', 'Hyejoong Kim', 'Wonseok Jeong', 'Chaehyeong Kim', 'Minjin Kim', 'Hyungjoo Chae'] | 2023-02-24 | null | null | null | null | ['response-generation'] | ['natural-language-processing'] | [ 3.21996808e-01 6.25031292e-01 -2.78646588e-01 -2.71546483e-01
-6.48947120e-01 -9.78612363e-01 5.11644721e-01 -1.46589383e-01
-2.69611012e-02 9.33221221e-01 -2.48106811e-02 -9.03598487e-01
5.59532881e-01 -7.82928467e-01 -4.16891843e-01 -4.12621886e-01
4.87642229e-01 7.04029500e-01 5.01118600e-01 -4.73460615... | [12.420877456665039, 8.115405082702637] |
7834e73b-0258-4cea-b2c5-6b23ee1d447c | a-critical-study-on-the-recent-deep-learning | 2111.01604 | null | https://arxiv.org/abs/2111.01604v1 | https://arxiv.org/pdf/2111.01604v1.pdf | A Critical Study on the Recent Deep Learning Based Semi-Supervised Video Anomaly Detection Methods | Video anomaly detection is one of the hot research topics in computer vision nowadays, as abnormal events contain a high amount of information. Anomalies are one of the main detection targets in surveillance systems, usually needing real-time actions. Regarding the availability of labeled data for training (i.e., there... | ['Robert Bergevin', 'Mohammad Baradaran'] | 2021-11-02 | null | null | null | null | ['supervised-anomaly-detection', 'semi-supervised-anomaly-detection'] | ['computer-vision', 'computer-vision'] | [ 7.34365582e-02 -3.09397548e-01 -2.22027346e-01 -2.30416164e-01
9.32344347e-02 -2.91966796e-01 4.09178197e-01 1.73542917e-01
-5.27686715e-01 2.96661437e-01 -2.58844048e-01 -1.07044749e-01
-1.15276035e-02 -6.64184690e-01 -4.59507138e-01 -1.01107740e+00
-2.75810301e-01 2.23904587e-02 4.34295416e-01 -3.42981428... | [7.874264717102051, 1.5453393459320068] |
c4890c9f-e86f-4680-b48f-580bfa91610e | text-guided-eyeglasses-manipulation-with | 2304.12539 | null | https://arxiv.org/abs/2304.12539v1 | https://arxiv.org/pdf/2304.12539v1.pdf | Text-guided Eyeglasses Manipulation with Spatial Constraints | Virtual try-on of eyeglasses involves placing eyeglasses of different shapes and styles onto a face image without physically trying them on. While existing methods have shown impressive results, the variety of eyeglasses styles is limited and the interactions are not always intuitive or efficient. To address these limi... | ['Wei Xu', 'Jingen Liu', 'Ping Liu', 'Jiacheng Wang'] | 2023-04-25 | null | null | null | null | ['virtual-try-on'] | ['computer-vision'] | [ 3.46497655e-01 3.24305706e-02 -9.55492333e-02 -1.40747398e-01
-1.74928859e-01 -7.95862675e-01 6.33638561e-01 -5.23240209e-01
-1.44308269e-01 5.81475437e-01 8.86308253e-02 -1.20320618e-01
1.28474668e-01 -3.83466721e-01 -7.20329225e-01 -7.82509565e-01
4.60575819e-01 -1.89576134e-01 1.75807461e-01 -2.81637043... | [12.687529563903809, -0.22697879374027252] |
c21e48ea-e818-41c5-b235-45d5c80d1ccb | textworld-a-learning-environment-for-text | 1806.11532 | null | https://arxiv.org/abs/1806.11532v2 | https://arxiv.org/pdf/1806.11532v2.pdf | TextWorld: A Learning Environment for Text-based Games | We introduce TextWorld, a sandbox learning environment for the training and evaluation of RL agents on text-based games. TextWorld is a Python library that handles interactive play-through of text games, as well as backend functions like state tracking and reward assignment. It comes with a curated list of games whose ... | ['James Moore', 'Ákos Kádár', 'Marc-Alexandre Côté', 'Tavian Barnes', 'Ben Kybartas', 'Xingdi Yuan', 'Mahmoud Adada', 'Layla El Asri', 'Emery Fine', 'Adam Trischler', 'Wendy Tay', 'Ruo Yu Tao', 'Matthew Hausknecht'] | 2018-06-29 | null | null | null | null | ['text-based-games'] | ['playing-games'] | [-3.38276297e-01 -4.79313545e-02 -7.07380660e-03 -4.84667085e-02
-6.06659353e-01 -9.06320333e-01 8.32733512e-01 -1.76305279e-01
-7.82394648e-01 1.02230442e+00 -4.12614122e-02 -5.11078775e-01
-7.63611048e-02 -8.95713210e-01 -4.66168642e-01 -4.81177866e-01
-4.58436519e-01 8.20848644e-01 6.42666161e-01 -1.03685832... | [3.724057197570801, 1.4171382188796997] |
0ef11e4d-e668-4ec6-8a3c-e16c006879d2 | breastscreening-on-the-use-of-multi-modality | 2004.03500 | null | https://arxiv.org/abs/2004.03500v2 | https://arxiv.org/pdf/2004.03500v2.pdf | BreastScreening: On the Use of Multi-Modality in Medical Imaging Diagnosis | This paper describes the field research, design and comparative deployment of a multimodal medical imaging user interface for breast screening. The main contributions described here are threefold: 1) The design of an advanced visual interface for multimodal diagnosis of breast cancer (BreastScreening); 2) Insights from... | ['Nuno Jardim Nunes', 'Jacinto Carlos Nascimento', 'Francisco Maria Calisto'] | 2020-04-07 | null | null | null | null | ['medical-image-retrieval', 'probabilistic-deep-learning', 'breast-cancer-detection', 'medical-image-retrieval', '3d-medical-imaging-segmentation', 'breast-cancer-detection', 'breast-tumour-classification', 'breast-mass-segmentation-in-whole-mammograms', 'automatic-machine-learning-model-selection', 'mathematical-proof... | ['computer-vision', 'computer-vision', 'knowledge-base', 'medical', 'medical', 'medical', 'medical', 'medical', 'methodology', 'miscellaneous'] | [ 2.68258691e-01 6.54234886e-01 -6.80349886e-01 -4.68674451e-01
-8.27603340e-01 -6.12058342e-01 2.68583983e-01 6.52444661e-01
-3.54507178e-01 2.09809795e-01 4.13423389e-01 -1.71898878e+00
-1.82824329e-01 -1.56456202e-01 -4.21356291e-01 -3.67404938e-01
-4.82631713e-01 5.48946798e-01 3.76758516e-01 5.34335300... | [15.1630277633667, -2.5372512340545654] |
d3e496cd-b7f2-4b79-ac71-b97e2cfc8f8b | lifelong-ensemble-learning-based-on-multiple | 2205.01982 | null | https://arxiv.org/abs/2205.01982v4 | https://arxiv.org/pdf/2205.01982v4.pdf | Lifelong Ensemble Learning based on Multiple Representations for Few-Shot Object Recognition | Service robots are integrating more and more into our daily lives to help us with various tasks. In such environments, robots frequently face new objects while working in the environment and need to learn them in an open-ended fashion. Furthermore, such robots must be able to recognize a wide range of object categories... | ['Songsong Xiong', 'Hamidreza Kasaei'] | 2022-05-04 | null | null | null | null | ['3d-object-recognition'] | ['computer-vision'] | [ 4.07102518e-02 -9.53869298e-02 1.58401296e-01 -3.83702755e-01
-5.20638406e-01 -3.37033838e-01 5.57995319e-01 -7.57310316e-02
-3.40190113e-01 5.66817641e-01 -1.81547716e-01 2.03000143e-01
-2.78926909e-01 -6.85450315e-01 -8.39336872e-01 -6.19297981e-01
-3.12865794e-01 7.17279851e-01 8.99987817e-02 -2.35284910... | [7.6367645263671875, -1.2324292659759521] |
fcf4101f-8e7d-434b-9709-e5f5f02f64cc | graph-propagation-transformer-for-graph | 2305.11424 | null | https://arxiv.org/abs/2305.11424v2 | https://arxiv.org/pdf/2305.11424v2.pdf | Graph Propagation Transformer for Graph Representation Learning | This paper presents a novel transformer architecture for graph representation learning. The core insight of our method is to fully consider the information propagation among nodes and edges in a graph when building the attention module in the transformer blocks. Specifically, we propose a new attention mechanism called... | ['Yue Qi', 'Cheng Cheng', 'Qiuying Peng', 'Tong Lu', 'Tianrun Shen', 'Tao Wang', 'Hao Tan', 'Zhe Chen'] | 2023-05-19 | null | null | null | null | ['graph-property-prediction', 'graph-regression', 'graph-representation-learning'] | ['graphs', 'graphs', 'methodology'] | [-3.47364634e-01 2.66067535e-01 -3.13931704e-01 -3.17986012e-01
-2.20554158e-01 -2.95621991e-01 5.57592988e-01 2.77070373e-01
9.70112160e-02 5.04162192e-01 2.01201975e-01 -6.38649225e-01
6.99670799e-03 -1.31992269e+00 -7.80135930e-01 -4.75034386e-01
-1.66583374e-01 4.40480322e-01 4.14454520e-01 -3.42012495... | [7.128396034240723, 6.305565357208252] |
39c3aad6-9323-4902-a551-409180f6b77b | semantic-interaction-in-augmented-reality | 2112.05846 | null | https://arxiv.org/abs/2112.05846v1 | https://arxiv.org/pdf/2112.05846v1.pdf | Semantic Interaction in Augmented Reality Environments for Microsoft HoloLens | Augmented Reality is a promising technique for human-machine interaction. Especially in robotics, which always considers systems in their environment, it is highly beneficial to display visualizations and receive user input directly in exactly that environment. We explore this idea using the Microsoft HoloLens, with wh... | ['Sven Behnke', 'Max Schwarz', 'Peer Schüett'] | 2021-11-18 | null | null | null | null | ['2d-semantic-segmentation'] | ['computer-vision'] | [ 3.92794788e-01 3.34687382e-01 4.45488900e-01 -1.94435641e-01
-2.40596473e-01 -7.99497008e-01 4.99281347e-01 2.89245963e-01
-3.55318785e-01 3.31784487e-01 -1.13737248e-01 -2.18655944e-01
-1.36676177e-01 -7.19093740e-01 -5.71867526e-01 -2.24798664e-01
-9.67988893e-02 7.37191796e-01 6.79698527e-01 -3.59689683... | [8.4495210647583, -2.7203011512756348] |
e460af21-12c5-47ef-bd6a-d9688aaea137 | computational-asymmetries-in-robust | 2306.14326 | null | https://arxiv.org/abs/2306.14326v1 | https://arxiv.org/pdf/2306.14326v1.pdf | Computational Asymmetries in Robust Classification | In the context of adversarial robustness, we make three strongly related contributions. First, we prove that while attacking ReLU classifiers is $\mathit{NP}$-hard, ensuring their robustness at training time is $\Sigma^2_P$-hard (even on a single example). This asymmetry provides a rationale for the fact that robust cl... | ['Michele Lombardi', 'Samuele Marro'] | 2023-06-25 | null | null | null | null | ['adversarial-robustness', 'classification-1'] | ['adversarial', 'methodology'] | [ 3.98155779e-01 5.12977660e-01 9.55207497e-02 -9.17287245e-02
-1.06905425e+00 -1.46593904e+00 3.82035911e-01 2.40090519e-01
-1.93092495e-01 9.39213634e-01 -3.37642908e-01 -9.09509480e-01
-4.54865098e-01 -1.27253008e+00 -1.27053177e+00 -6.87836885e-01
-4.23478991e-01 -8.11455622e-02 3.95849496e-01 -5.72573185... | [5.818338871002197, 7.670707702636719] |
025f91d0-190e-4414-8a4b-61ea126732d7 | global-to-local-expression-aware-embeddings | 2210.15160 | null | https://arxiv.org/abs/2210.15160v2 | https://arxiv.org/pdf/2210.15160v2.pdf | Global-to-local Expression-aware Embeddings for Facial Action Unit Detection | Expressions and facial action units (AUs) are two levels of facial behavior descriptors. Expression auxiliary information has been widely used to improve the AU detection performance. However, most existing expression representations can only describe pre-determined discrete categories (e.g., Angry, Disgust, Happy, Sad... | ['Yu Ding', 'Zhigang Deng', 'Wei Chen', 'Hao Zeng', 'Wei zhang', 'Rudong An'] | 2022-10-27 | null | null | null | null | ['action-unit-detection', 'facial-action-unit-detection'] | ['computer-vision', 'computer-vision'] | [ 2.77591109e-01 -2.10296303e-01 -1.59252495e-01 -6.72497034e-01
-4.75382000e-01 -1.84823319e-01 5.33652484e-01 -3.10314745e-01
-2.42024541e-01 3.58012229e-01 1.33461460e-01 4.30388212e-01
2.56320804e-01 -7.30058610e-01 -2.39099786e-01 -1.04501152e+00
1.52914494e-01 -3.05455178e-01 -3.18712056e-01 -4.10825014... | [13.634353637695312, 1.6669858694076538] |
5ac34277-8b67-4d90-8671-9c2574195de8 | sign-segmentation-with-changepoint-modulated | 2104.13817 | null | https://arxiv.org/abs/2104.13817v1 | https://arxiv.org/pdf/2104.13817v1.pdf | Sign Segmentation with Changepoint-Modulated Pseudo-Labelling | The objective of this work is to find temporal boundaries between signs in continuous sign language. Motivated by the paucity of annotation available for this task, we propose a simple yet effective algorithm to improve segmentation performance on unlabelled signing footage from a domain of interest. We make the follow... | ['Samuel Albanie', 'Gül Varol', 'Neil Fox', 'Nicolaj C. Stache', 'Katrin Renz'] | 2021-04-28 | null | null | null | null | ['source-free-domain-adaptation'] | ['computer-vision'] | [ 6.55192733e-01 -2.06900224e-01 -2.20060840e-01 -5.04979432e-01
-1.04578352e+00 -9.37916458e-01 7.40726948e-01 -7.25459933e-01
-8.84321630e-01 7.90319920e-01 5.10043502e-01 -2.18182504e-01
-7.37021416e-02 2.13053431e-02 -5.15297771e-01 -5.41327477e-01
-1.63657535e-02 4.52406108e-01 7.82891452e-01 -1.39459074... | [9.124917984008789, -6.51456356048584] |
1e5f6a22-2e8a-405f-901e-47e00efc50dc | local-model-reconstruction-attacks-in | 2210.16205 | null | https://arxiv.org/abs/2210.16205v2 | https://arxiv.org/pdf/2210.16205v2.pdf | Local Model Reconstruction Attacks in Federated Learning and their Uses | In this paper, we initiate the study of local model reconstruction attacks for federated learning, where a honest-but-curious adversary eavesdrops the messages exchanged between a targeted client and the server, and then reconstructs the local/personalized model of the victim. The local model reconstruction attack allo... | ['Eoin Thomas', 'Frederic Giroire', 'Giovanni Neglia', 'Chuan Xu', 'Ilias Driouich'] | 2022-10-28 | null | null | null | null | ['inference-attack'] | ['adversarial'] | [-7.62479082e-02 2.03246087e-01 -3.00860077e-01 -6.12115681e-01
-1.10670030e+00 -1.07887352e+00 3.07057947e-01 9.73109305e-02
-1.81198210e-01 6.05757892e-01 -1.63875774e-01 -3.81031662e-01
-2.13575780e-01 -1.19340599e+00 -9.66933429e-01 -1.05243456e+00
-2.52152801e-01 6.41444921e-01 -2.95204818e-02 5.88283464... | [5.802190780639648, 6.805213451385498] |
b2841af9-0884-49c1-928f-d7cdcc9632fd | reward-free-policy-imitation-learning-for | 2304.07988 | null | https://arxiv.org/abs/2304.07988v1 | https://arxiv.org/pdf/2304.07988v1.pdf | Reward-free Policy Imitation Learning for Conversational Search | Existing conversational search studies mainly focused on asking better clarifying questions and/or improving search result quality. These works aim at retrieving better responses according to the search context, and their performances are evaluated on either single-turn tasks or multi-turn tasks under naive conversatio... | ['Qingyao Ai', 'Zhichao Xu', 'Zhenduo Wang'] | 2023-04-17 | null | null | null | null | ['conversational-search'] | ['natural-language-processing'] | [ 6.07930534e-02 1.42136663e-01 -4.59157646e-01 -2.85965353e-01
-1.05347514e+00 -7.42042780e-01 9.35783029e-01 -3.20684552e-01
-5.33912599e-01 7.09464371e-01 4.77178037e-01 -4.41817045e-01
-2.96179295e-01 -3.72188061e-01 -1.59878582e-01 -5.16845465e-01
4.13510680e-01 8.97351027e-01 1.48196056e-01 -6.70412838... | [12.136240005493164, 7.827614784240723] |
93d36079-224a-4a4f-8e01-c80d96011ed9 | h-net-unsupervised-attention-based-stereo | 2104.11288 | null | https://arxiv.org/abs/2104.11288v1 | https://arxiv.org/pdf/2104.11288v1.pdf | H-Net: Unsupervised Attention-based Stereo Depth Estimation Leveraging Epipolar Geometry | Depth estimation from a stereo image pair has become one of the most explored applications in computer vision, with most of the previous methods relying on fully supervised learning settings. However, due to the difficulty in acquiring accurate and scalable ground truth data, the training of fully supervised methods is... | ['Daniel S. Elson', 'Stamatia Giannarou', 'Jian-Qing Zheng', 'Baoru Huang'] | 2021-04-22 | null | null | null | null | ['stereo-depth-estimation'] | ['computer-vision'] | [ 2.11215928e-01 3.12798291e-01 1.81036294e-02 -3.91420782e-01
-4.37748224e-01 1.14557706e-02 5.60121179e-01 -6.97996542e-02
-5.44689000e-01 5.43692172e-01 3.94780546e-01 2.62864560e-01
2.38210410e-02 -8.41329396e-01 -6.75606132e-01 -7.71231890e-01
5.01146972e-01 3.63454849e-01 3.12051982e-01 -1.05873324... | [8.735236167907715, -2.406327486038208] |
0956c109-8158-4b9b-9dcb-23faab38b112 | automatic-generation-of-board-game-manuals | 2109.09507 | null | https://arxiv.org/abs/2109.09507v1 | https://arxiv.org/pdf/2109.09507v1.pdf | Automatic Generation of Board Game Manuals | In this paper we present a process for automatically generating manuals for board games within the Ludii general game system. This process requires many different sub-tasks to be addressed, such as English translation of Ludii game descriptions, move visualisation, highlighting winning moves, strategy explanation, amon... | ['Cameron Browne', 'Dennis J. N. J. Soemers', 'Eric Piette', 'Matthew Stephenson'] | 2021-09-20 | null | null | null | null | ['board-games'] | ['playing-games'] | [-1.42873049e-01 4.94330943e-01 3.57640594e-01 3.99792120e-02
-2.11181909e-01 -1.01069915e+00 5.55151403e-01 -9.04987287e-03
-3.21827978e-01 6.48453474e-01 4.39371206e-02 -8.66119504e-01
-3.34340483e-01 -6.42781556e-01 2.26031840e-01 -1.42615527e-01
2.56331652e-01 6.55330896e-01 5.01223981e-01 -8.59476566... | [3.419140100479126, 1.4863507747650146] |
25c4ba4d-6003-462e-81c5-90ace0601bd9 | sub-8-bit-quantization-of-streaming-keyword | 2207.06920 | null | https://arxiv.org/abs/2207.06920v2 | https://arxiv.org/pdf/2207.06920v2.pdf | Sub 8-Bit Quantization of Streaming Keyword Spotting Models for Embedded Chipsets | We propose a novel 2-stage sub 8-bit quantization aware training algorithm for all components of a 250K parameter feedforward, streaming, state-free keyword spotting model. For the 1st-stage, we adapt a recently proposed quantization technique using a non-linear transformation with tanh(.) on dense layer weights. In th... | ['Santosh Kumar Cheekatmalla', 'Shiv Vitaladevuni', 'Nikko Strom', 'Alex Escott', 'Yuzong Liu', 'Sree Hari Krishnan Parthasarathi', 'Lu Zeng'] | 2022-07-13 | null | null | null | null | ['keyword-spotting'] | ['speech'] | [ 3.14688325e-01 -1.84524000e-01 -2.08182767e-01 -3.04169744e-01
-5.51820755e-01 -4.30018336e-01 1.30635411e-01 4.10558313e-01
-9.93756533e-01 4.02272642e-01 -4.53671589e-02 -8.12383413e-01
-7.47342631e-02 -6.00665987e-01 -6.78883612e-01 -6.30256176e-01
-4.97061312e-01 -5.47081754e-02 4.35448438e-01 -1.09810844... | [8.520773887634277, 2.9727752208709717] |
3a7096c2-2f17-405b-90e9-f11efc3a469d | history-based-unsupervised-data-oriented | null | null | https://aclanthology.org/R13-1059 | https://aclanthology.org/R13-1059.pdf | History Based Unsupervised Data Oriented Parsing | null | ['Gholamreza Ghasem-Sani', 'Mohsen Mesgar'] | 2013-09-01 | history-based-unsupervised-data-oriented-1 | https://aclanthology.org/R13-1059 | https://aclanthology.org/R13-1059.pdf | ranlp-2013-9 | ['lexical-analysis'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.387378215789795, 3.6384708881378174] |
a26f2933-6b08-4274-840c-1652c24f58f2 | extreme-learning-machine-based-heterogeneous | null | null | https://ieeexplore.ieee.org/document/8700294 | https://ieeexplore.ieee.org/document/8700294 | Extreme Learning Machine-Based Heterogeneous Domain Adaptation for Classification of Hyperspectral Images | An extreme learning machine (ELM)-based heterogeneous
domain adaptation (HDA) algorithm is proposed for the
classification of remote sensing images. In the adaptive ELM
network, one hidden layer is used for the source data to provide
the random features, whereas two hidden layers are set for target
data to produce... | ['Li Zhou', 'Li Ma'] | 2019-11-01 | null | null | null | ieee-geoscience-and-remote-sensing-letters | ['classification-of-hyperspectral-images'] | ['computer-vision'] | [ 1.64974183e-01 -1.94654986e-01 5.12163304e-02 -4.02976573e-01
-4.44183856e-01 -5.11626601e-02 2.79762566e-01 -2.10779458e-01
-2.04177856e-01 4.40134704e-01 -1.51263848e-01 -4.01318707e-02
-3.78138989e-01 -9.50802088e-01 -2.34373361e-01 -1.34507906e+00
2.84560956e-02 2.15754330e-01 -2.70628780e-01 -1.45799845... | [9.966907501220703, -1.5246762037277222] |
19f101d4-5642-4671-90f5-161ee464979e | weakly-supervised-conditional-embedding-for | 2306.02928 | null | https://arxiv.org/abs/2306.02928v1 | https://arxiv.org/pdf/2306.02928v1.pdf | Weakly-Supervised Conditional Embedding for Referred Visual Search | This paper presents a new approach to image similarity search in the context of fashion, a domain with inherent ambiguity due to the multiple ways in which images can be considered similar. We introduce the concept of Referred Visual Search (RVS), where users provide additional information to define the desired similar... | ['David Picard', 'Jérémie Mary', 'Simon Lepage'] | 2023-06-05 | null | null | null | null | ['image-similarity-search'] | ['computer-vision'] | [-1.62587501e-02 -4.78149146e-01 -4.18689758e-01 -2.60852188e-01
-9.93746996e-01 -9.60263669e-01 8.71926606e-01 4.99721587e-01
-4.70934778e-01 1.04431644e-01 3.01538050e-01 -7.65118301e-02
-1.21759780e-01 -2.70748377e-01 -6.82334185e-01 -4.17722434e-01
2.21153423e-01 3.03366870e-01 1.58378929e-01 -2.55605459... | [10.8936767578125, 1.1003391742706299] |
0868e6e4-784d-428a-bafd-a8276e3797e5 | degreembed-incorporating-entity-embedding | 2112.09933 | null | https://arxiv.org/abs/2112.09933v2 | https://arxiv.org/pdf/2112.09933v2.pdf | DegreEmbed: incorporating entity embedding into logic rule learning for knowledge graph reasoning | Knowledge graphs (KGs), as structured representations of real world facts, are intelligent databases incorporating human knowledge that can help machine imitate the way of human problem solving. However, KGs are usually huge and there are inevitably missing facts in KGs, thus undermining applications such as question a... | ['Yuliang Wei', 'Guodong Xin', 'Yao Wang', 'Hongri Liu', 'Haotian Li'] | 2021-12-18 | null | null | null | null | ['link-prediction', 'knowledge-graphs', 'question-answering'] | ['graphs', 'knowledge-base', 'natural-language-processing'] | [-5.03334701e-01 8.25224280e-01 -8.72859240e-01 -1.60287589e-01
3.53042483e-01 -2.86256909e-01 2.78372794e-01 6.64173841e-01
3.09349895e-01 8.19259346e-01 3.57484609e-01 -5.85696995e-01
-7.27583408e-01 -1.66992116e+00 -7.52251625e-01 -2.21037775e-01
-4.62603450e-01 8.01785171e-01 5.21142900e-01 -4.09318447... | [8.831267356872559, 7.8418145179748535] |
76de9d4c-d5bc-427b-850a-185cc860c251 | immfusion-robust-mmwave-rgb-fusion-for-3d | 2210.01346 | null | https://arxiv.org/abs/2210.01346v1 | https://arxiv.org/pdf/2210.01346v1.pdf | ImmFusion: Robust mmWave-RGB Fusion for 3D Human Body Reconstruction in All Weather Conditions | 3D human reconstruction from RGB images achieves decent results in good weather conditions but degrades dramatically in rough weather. Complementary, mmWave radars have been employed to reconstruct 3D human joints and meshes in rough weather. However, combining RGB and mmWave signals for robust all-weather 3D human rec... | ['Qi Ye', 'Yuchi Huo', 'Jiming Chen', 'Bin Fang', 'Yingfeng Chen', 'Shaohao Zhu', 'Kun Shi', 'Xiangyu Wang', 'Anjun Chen'] | 2022-10-04 | null | null | null | null | ['3d-human-reconstruction'] | ['computer-vision'] | [-7.76998326e-02 -3.78764749e-01 3.06604534e-01 -2.78937340e-01
-7.90206552e-01 -2.77005225e-01 2.99641818e-01 -3.35019886e-01
-3.22957158e-01 4.57683712e-01 1.57190308e-01 4.32922989e-01
2.15894178e-01 -1.18381846e+00 -6.04157209e-01 -8.11896861e-01
1.07274905e-01 7.16210961e-01 2.35150293e-01 -4.47627664... | [7.1825480461120605, -1.2461543083190918] |
2f0a9992-c93e-473c-8ee1-afa6cbbd19b4 | invero-xl-making-cross-lingual-semantic-role | null | null | https://aclanthology.org/2021.emnlp-demo.36 | https://aclanthology.org/2021.emnlp-demo.36.pdf | InVeRo-XL: Making Cross-Lingual Semantic Role Labeling Accessible with Intelligible Verbs and Roles | Notwithstanding the growing interest in cross-lingual techniques for Natural Language Processing, there has been a surprisingly small number of efforts aimed at the development of easy-to-use tools for cross-lingual Semantic Role Labeling. In this paper, we fill this gap and present InVeRo-XL, an off-the-shelf state-of... | ['Roberto Navigli', 'Francesco Cecconi', 'Fabrizio Brignone', 'Riccardo Orlando', 'Simone Conia'] | null | null | null | null | emnlp-acl-2021-11 | ['semantic-role-labeling'] | ['natural-language-processing'] | [ 8.08760375e-02 1.98725477e-01 -5.25615215e-01 -5.80036640e-01
-8.59667778e-01 -1.10991824e+00 6.40559316e-01 5.75674474e-01
-5.02561986e-01 9.24746335e-01 4.34430748e-01 -6.02283657e-01
-1.47569686e-01 -5.80914795e-01 -3.54279280e-01 -2.73507982e-01
2.43254721e-01 7.32017457e-01 4.50040221e-01 -6.49380982... | [10.328627586364746, 9.451967239379883] |
f8665bad-a6b9-4ca4-9300-7ce5fc039273 | mmc-multi-modal-colorization-of-images-using | 2304.11993 | null | https://arxiv.org/abs/2304.11993v2 | https://arxiv.org/pdf/2304.11993v2.pdf | MMC: Multi-Modal Colorization of Images using Textual Descriptions | Handling various objects with different colors is a significant challenge for image colorization techniques. Thus, for complex real-world scenes, the existing image colorization algorithms often fail to maintain color consistency. In this work, we attempt to integrate textual descriptions as an auxiliary condition, alo... | ['Michael Blumenstein', 'Umapada Pal', 'Saumik Bhattacharya', 'Prasun Roy', 'Subhankar Ghosh'] | 2023-04-24 | null | null | null | null | ['colorization'] | ['computer-vision'] | [ 4.02873248e-01 -5.06154776e-01 1.60458520e-01 -3.06960791e-01
-3.73258650e-01 -5.26588678e-01 2.24613473e-01 1.31947786e-01
-3.61201882e-01 6.27981901e-01 -1.49240419e-01 -1.30079746e-01
2.08703905e-01 -7.38668799e-01 -6.21799290e-01 -7.90534675e-01
5.41180551e-01 2.94948090e-02 1.48574129e-01 -4.90381196... | [11.191059112548828, -1.237737774848938] |
11d37e9d-27b4-48d4-8d1f-9a7acdfb5481 | schema-guided-semantic-accuracy-faithfulness | 2301.12568 | null | https://arxiv.org/abs/2301.12568v1 | https://arxiv.org/pdf/2301.12568v1.pdf | Schema-Guided Semantic Accuracy: Faithfulness in Task-Oriented Dialogue Response Generation | Ensuring that generated utterances are faithful to dialogue actions is crucial for Task-Oriented Dialogue Response Generation. Slot Error Rate (SER) only partially measures generation quality in that it solely assesses utterances generated from non-categorical slots whose values are expected to be reproduced exactly. U... | ['Bill Byrne', 'Weizhe Lin', 'Jinghong Chen'] | 2023-01-29 | null | null | null | null | ['response-generation'] | ['natural-language-processing'] | [ 5.29494941e-01 9.63757098e-01 2.06695706e-01 -6.79300725e-01
-1.34959590e+00 -8.50364745e-01 1.01461387e+00 -9.04821977e-02
-1.42597437e-01 1.05639744e+00 8.33931327e-01 -3.41518521e-01
9.26824212e-02 -8.06574345e-01 -2.76857466e-01 -8.37138221e-02
6.36125684e-01 1.16468871e+00 3.13056618e-01 -9.56869662... | [12.729377746582031, 8.082128524780273] |
b33d0921-fd85-4cab-a5aa-3685eded5a45 | silent-killer-optimizing-backdoor-trigger | 2301.02615 | null | https://arxiv.org/abs/2301.02615v1 | https://arxiv.org/pdf/2301.02615v1.pdf | Silent Killer: Optimizing Backdoor Trigger Yields a Stealthy and Powerful Data Poisoning Attack | We propose a stealthy and powerful backdoor attack on neural networks based on data poisoning (DP). In contrast to previous attacks, both the poison and the trigger in our method are stealthy. We are able to change the model's classification of samples from a source class to a target class chosen by the attacker. We do... | ['Lior Rokach', 'Gallil Maimon', 'Tzvi Lederer'] | 2023-01-05 | null | null | null | null | ['data-poisoning'] | ['adversarial'] | [ 4.43043560e-01 6.49394765e-02 -5.98633215e-02 2.01315340e-02
-6.72360659e-01 -1.29214454e+00 8.48715007e-01 -2.78732032e-01
-7.51812875e-01 8.03123653e-01 -2.26206332e-01 -6.95361674e-01
4.39747691e-01 -7.94711828e-01 -1.14706457e+00 -9.20723796e-01
9.50632710e-03 3.34824741e-01 3.27257037e-01 -3.92232597... | [5.837579250335693, 7.658257961273193] |
8410cc49-0384-4394-94fd-232c6958e5a0 | 190909946 | 1909.09946 | null | https://arxiv.org/abs/1909.09946v1 | https://arxiv.org/pdf/1909.09946v1.pdf | Semi-supervised estimation of event temporal length for cell event detection | Cell event detection in cell videos is essential for monitoring of cellular behavior over extended time periods. Deep learning methods have shown great success in the detection of cell events for their ability to capture more discriminative features of cellular processes compared to traditional methods. In particular, ... | ['Jinman Kim', 'Michael Fulham', 'Ashnil Kumar', 'Ha Tran Hong Phan', 'David Feng'] | 2019-09-22 | null | null | null | null | ['mitosis-detection'] | ['medical'] | [ 3.56413811e-01 -2.27303594e-01 -6.65257201e-02 1.53726218e-02
-8.17361414e-01 -4.61202174e-01 5.63666523e-01 5.01378596e-01
-9.43412781e-01 8.69512439e-01 -1.55540049e-01 -3.44884545e-02
2.73377061e-01 -6.75443113e-01 -9.21846926e-01 -1.16041839e+00
-2.29182601e-01 4.91203964e-01 4.54414397e-01 4.58645493... | [14.54472541809082, -3.209603786468506] |
f76b8532-57af-4946-97cc-aac7f6b5a9d6 | look-before-you-leap-improving-text-based | 2209.06209 | null | https://arxiv.org/abs/2209.06209v1 | https://arxiv.org/pdf/2209.06209v1.pdf | Look Before You Leap: Improving Text-based Person Retrieval by Learning A Consistent Cross-modal Common Manifold | The core problem of text-based person retrieval is how to bridge the heterogeneous gap between multi-modal data. Many previous approaches contrive to learning a latent common manifold mapping paradigm following a \textbf{cross-modal distribution consensus prediction (CDCP)} manner. When mapping features from distributi... | ['Yifeng Li', 'Tian Wang', 'Chao Liu', 'Xili Wan', 'Jingyi Xue', 'Aichun Zhu', 'Zijie Wang'] | 2022-09-13 | null | null | null | null | ['person-retrieval', 'nlp-based-person-retrival'] | ['computer-vision', 'computer-vision'] | [-3.21878076e-01 -5.12938142e-01 -1.25431612e-01 -1.87919408e-01
-9.07353342e-01 -4.58161384e-01 7.72646308e-01 -8.04911479e-02
-3.06263775e-01 4.35823143e-01 3.35582435e-01 2.56870002e-01
-5.21256447e-01 -6.61157906e-01 -3.86937797e-01 -9.01656032e-01
2.91233420e-01 6.04233980e-01 -1.09946594e-01 -3.68533105... | [14.533700942993164, 0.8887861371040344] |
9ab96587-08a8-418c-ac3c-03640d0c8af0 | surrogate-assisted-active-subspace-and-active | 2105.04979 | null | https://arxiv.org/abs/2105.04979v2 | https://arxiv.org/pdf/2105.04979v2.pdf | Surrogate assisted active subspace and active subspace assisted surrogate -- A new paradigm for high dimensional structural reliability analysis | Performing reliability analysis on complex systems is often computationally expensive. In particular, when dealing with systems having high input dimensionality, reliability estimation becomes a daunting task. A popular approach to overcome the problem associated with time-consuming and expensive evaluations is buildin... | ['Navaneeth N.', 'Souvik Chakraborty'] | 2021-05-11 | null | null | null | null | ['sparse-learning'] | ['methodology'] | [ 1.50296912e-01 -5.33464551e-02 1.56732023e-01 2.69611347e-02
-8.86834860e-01 -3.13419163e-01 2.49955416e-01 -8.36370364e-02
1.54606085e-02 8.19450736e-01 1.81826651e-01 8.65482092e-02
-6.31187737e-01 -3.34782779e-01 -5.70741534e-01 -1.03216493e+00
-9.43961218e-02 2.70972997e-01 -3.84017617e-01 6.24161065... | [7.782650470733643, 4.226172924041748] |
3988f1fa-5f44-466a-9658-f6aff07c58ac | group-aware-label-transfer-for-domain | 2103.12366 | null | https://arxiv.org/abs/2103.12366v1 | https://arxiv.org/pdf/2103.12366v1.pdf | Group-aware Label Transfer for Domain Adaptive Person Re-identification | Unsupervised Domain Adaptive (UDA) person re-identification (ReID) aims at adapting the model trained on a labeled source-domain dataset to a target-domain dataset without any further annotations. Most successful UDA-ReID approaches combine clustering-based pseudo-label prediction with representation learning and perfo... | ['Zheng-Jun Zha', 'Jiebo Luo', 'Tao Mei', 'Lingxiao He', 'Wu Liu', 'Kecheng Zheng'] | 2021-03-23 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Zheng_Group-aware_Label_Transfer_for_Domain_Adaptive_Person_Re-identification_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Zheng_Group-aware_Label_Transfer_for_Domain_Adaptive_Person_Re-identification_CVPR_2021_paper.pdf | cvpr-2021-1 | ['online-clustering'] | ['computer-vision'] | [ 3.66959006e-01 -8.65881704e-03 -1.97115511e-01 -7.13106632e-01
-1.02653408e+00 -6.57094419e-01 5.71189702e-01 9.28241685e-02
-5.93942821e-01 8.09676409e-01 -1.83808655e-01 -4.79728309e-03
-3.14846754e-01 -6.55359983e-01 -4.72963631e-01 -7.77331293e-01
1.57242924e-01 1.02346599e+00 -1.83336750e-01 1.33221924... | [14.836263656616211, 1.0986216068267822] |
4ede4c6b-691e-467a-b264-81a24608b1ac | a-simple-probabilistic-model-for-uncertainty | 1807.09312 | null | http://arxiv.org/abs/1807.09312v1 | http://arxiv.org/pdf/1807.09312v1.pdf | A Simple Probabilistic Model for Uncertainty Estimation | The article focuses on determining the predictive uncertainty of a model on
the example of atrial fibrillation detection problem by a single-lead ECG
signal. To this end, the model predicts parameters of the beta distribution
over class probabilities instead of these probabilities themselves. It was
shown that the desc... | ['Alexander Kuvaev', 'Roman Khudorozhkov'] | 2018-07-24 | null | null | null | null | ['atrial-fibrillation-detection'] | ['medical'] | [ 4.50590968e-01 5.66505313e-01 -5.78696914e-02 -6.73970938e-01
-8.52293491e-01 -5.97227275e-01 8.95040482e-02 2.23166317e-01
-4.31337021e-02 1.36031830e+00 -2.96808243e-01 -7.10439444e-01
-5.25093079e-01 -3.29996049e-01 -1.96170092e-01 -7.39633381e-01
-5.68216503e-01 7.55829513e-01 -5.20339832e-02 5.52102864... | [14.163003921508789, 3.2433977127075195] |
f8d2ddc0-89d3-4fd7-8589-6745b6bb5057 | differentially-private-episodic-reinforcement | 2306.01121 | null | https://arxiv.org/abs/2306.01121v2 | https://arxiv.org/pdf/2306.01121v2.pdf | Differentially Private Episodic Reinforcement Learning with Heavy-tailed Rewards | In this paper, we study the problem of (finite horizon tabular) Markov decision processes (MDPs) with heavy-tailed rewards under the constraint of differential privacy (DP). Compared with the previous studies for private reinforcement learning that typically assume rewards are sampled from some bounded or sub-Gaussian ... | ['Di Wang', 'Sayak Ray Chowdhury', 'Xingyu Zhou', 'Yulian Wu'] | 2023-06-01 | null | null | null | null | ['multi-armed-bandits'] | ['miscellaneous'] | [ 4.79390845e-02 3.14583570e-01 -6.21554613e-01 -4.25144255e-01
-1.22243607e+00 -7.79847443e-01 3.71325538e-02 -6.84587751e-03
-5.56160152e-01 1.39754796e+00 1.63972795e-01 -5.51862776e-01
-5.71919680e-01 -8.82866204e-01 -8.28888655e-01 -1.16074216e+00
-1.23580188e-01 4.09480900e-01 -3.57557118e-01 2.82583963... | [4.529442310333252, 3.317413568496704] |
c6d4d732-0c2e-40f2-8533-796ba16add29 | dyngfn-bayesian-dynamic-causal-discovery | 2302.04178 | null | https://arxiv.org/abs/2302.04178v2 | https://arxiv.org/pdf/2302.04178v2.pdf | DynGFN: Towards Bayesian Inference of Gene Regulatory Networks with GFlowNets | One of the grand challenges of cell biology is inferring the gene regulatory network (GRN) which describes interactions between genes and their products that control gene expression and cellular function. We can treat this as a causal discovery problem but with two non-standard challenges: (1) regulatory networks are i... | ['Yoshua Bengio', 'Bo wang', 'Leo J. Lee', 'Jason Hartford', 'Alexander Tong', 'Lazar Atanackovic'] | 2023-02-08 | null | null | null | null | ['causal-discovery'] | ['knowledge-base'] | [ 3.43992054e-01 4.44315374e-02 -3.46605659e-01 -1.35885682e-02
-5.58388233e-01 -8.19812715e-01 7.41776288e-01 -5.81064261e-02
9.10939202e-02 8.55371416e-01 5.17516553e-01 -4.89886850e-01
-6.54213667e-01 -6.88679397e-01 -9.34635758e-01 -9.31904197e-01
-5.69663405e-01 1.05197096e+00 2.56659184e-02 6.91069216... | [7.126335144042969, 4.880430221557617] |
ce805814-9f8c-4512-9521-80447a75f4c0 | data-efficient-visual-place-recognition-using | 2209.08343 | null | https://arxiv.org/abs/2209.08343v2 | https://arxiv.org/pdf/2209.08343v2.pdf | Data Efficient Visual Place Recognition Using Extremely JPEG-Compressed Images | Visual Place Recognition (VPR) is the ability of a robotic platform to correctly interpret visual stimuli from its on-board cameras in order to determine whether it is currently located in a previously visited place, despite different viewpoint, illumination and appearance changes. JPEG is a widely used image compressi... | ['Shoaib Ehsan', 'Klaus McDonald-Maier', 'Michael Milford', 'Bruno Ferrarini', 'Mihnea-Alexandru Tomita'] | 2022-09-17 | null | null | null | null | ['visual-place-recognition'] | ['computer-vision'] | [ 6.65965438e-01 -8.93960744e-02 1.98034480e-01 9.12564062e-03
-2.45758548e-01 -5.76217413e-01 7.11268008e-01 3.35934132e-01
-8.55736434e-01 4.62720037e-01 -3.56659085e-01 -1.62617847e-01
-1.29738897e-01 -7.64277577e-01 -1.02182937e+00 -5.48713148e-01
-2.11734831e-01 1.39869317e-01 3.70852351e-01 -3.96998048... | [7.559793949127197, -1.8618576526641846] |
558c739e-c014-4954-9adb-f8f7af365941 | using-language-models-to-improve-rule-based | null | null | https://aclanthology.org/2022.latechclfl-1.5 | https://aclanthology.org/2022.latechclfl-1.5.pdf | Using Language Models to Improve Rule-based Linguistic Annotation of Modern Historical Japanese Corpora | Annotation of unlabeled textual corpora with linguistic metadata is a fundamental technology in many scholarly workflows in the digital humanities (DH). Pretrained natural language processing pipelines offer tokenization, tagging, and dependency parsing of raw text simultaneously using an annotation scheme like Univers... | ['Mitsunori Ogihara', 'Jerry Bonnell'] | null | null | null | null | latechclfl-coling-2022-10 | ['dependency-parsing'] | ['natural-language-processing'] | [-5.31664938e-02 1.93073913e-01 -5.32942593e-01 -4.56578940e-01
-1.18802440e+00 -9.01364863e-01 8.51572096e-01 3.75934154e-01
-7.82673776e-01 8.75195742e-01 6.31716192e-01 -5.32267332e-01
-2.43534632e-02 -2.81095684e-01 -4.14747417e-01 -1.93292141e-01
3.28680664e-01 8.19090366e-01 3.21357906e-01 -2.20128596... | [10.085917472839355, 9.748867988586426] |
2f67a72a-45e2-4c78-9889-2072b026ef9d | inference-in-predictive-quantile-regressions | 2306.00296 | null | https://arxiv.org/abs/2306.00296v1 | https://arxiv.org/pdf/2306.00296v1.pdf | Inference in Predictive Quantile Regressions | This paper studies inference in predictive quantile regressions when the predictive regressor has a near-unit root. We derive asymptotic distributions for the quantile regression estimator and its heteroskedasticity and autocorrelation consistent (HAC) t-statistic in terms of functionals of Ornstein-Uhlenbeck processes... | ['Nina Kuriyama', 'Katsumi Shimotsu', 'Alex Maynard'] | 2023-06-01 | null | null | null | null | ['unity'] | ['computer-vision'] | [-2.32170895e-01 -3.24908912e-01 -2.30490267e-01 -2.68142462e-01
-7.77275801e-01 -6.05551898e-01 6.38553560e-01 -1.77729815e-01
-2.41101071e-01 1.32427394e+00 1.33974299e-01 -7.94858038e-01
-5.47590971e-01 -8.79905462e-01 -5.08500278e-01 -8.41681421e-01
-2.08953917e-01 4.08266544e-01 2.85554416e-02 1.16338173... | [6.232591152191162, 4.1286821365356445] |
82487c95-ce5d-4ef7-ad63-81932c4cd774 | recurrent-residual-convolutional-neural | 1802.06955 | null | http://arxiv.org/abs/1802.06955v5 | http://arxiv.org/pdf/1802.06955v5.pdf | Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation | Deep learning (DL) based semantic segmentation methods have been providing
state-of-the-art performance in the last few years. More specifically, these
techniques have been successfully applied to medical image classification,
segmentation, and detection tasks. One deep learning technique, U-Net, has
become one of the ... | ['Mahmudul Hasan', 'Tarek M. Taha', 'Md Zahangir Alom', 'Vijayan K. Asari', 'Chris Yakopcic'] | 2018-02-20 | null | null | null | null | ['skin-cancer-segmentation', 'lung-nodule-segmentation'] | ['medical', 'medical'] | [ 2.21088767e-01 7.67758787e-02 -2.45395854e-01 -1.32063970e-01
-1.45571575e-01 3.55346985e-02 8.94787908e-02 -2.29115516e-01
-4.30015951e-01 5.64417481e-01 1.37203112e-01 -3.03532451e-01
2.39276618e-01 -1.00840533e+00 -2.69508898e-01 -6.62380099e-01
2.38856480e-01 -1.85377926e-01 7.09784865e-01 -7.63174072... | [14.812722206115723, -2.7027487754821777] |
a7382a37-8931-47a0-8599-c68f58d9545e | gantee-generative-adversatial-network-for | 2303.14480 | null | https://arxiv.org/abs/2303.14480v1 | https://arxiv.org/pdf/2303.14480v1.pdf | GANTEE: Generative Adversatial Network for Taxonomy Entering Evaluation | Taxonomy is formulated as directed acyclic concepts graphs or trees that support many downstream tasks. Many new coming concepts need to be added to an existing taxonomy. The traditional taxonomy expansion task aims only at finding the best position for new coming concepts in the existing taxonomy. However, they have t... | ['Jian Zhong', 'Jiaqing Liang', 'Zhixu Li', 'Hongwei Feng', 'Yanghua Xiao', 'Jingping Liu', 'Sihang Jiang', 'Zhouhong Gu'] | 2023-03-25 | null | null | null | null | ['taxonomy-expansion'] | ['natural-language-processing'] | [-3.46850716e-02 -1.20966382e-01 -4.84682657e-02 -3.51286054e-01
-2.28902295e-01 -5.84636271e-01 4.52677935e-01 1.70590475e-01
-5.62036157e-01 6.77644193e-01 1.43423468e-01 -2.58382499e-01
-1.86960012e-01 -1.32822907e+00 -1.25525713e-01 -6.12487078e-01
1.78884611e-01 7.15730608e-01 1.94038570e-01 -5.14133573... | [9.25893497467041, 8.093376159667969] |
49ff4b31-7d84-4fb7-bdaa-a7538dae3776 | on-the-use-of-emojis-to-train-emotion | 1902.08906 | null | http://arxiv.org/abs/1902.08906v2 | http://arxiv.org/pdf/1902.08906v2.pdf | On the Use of Emojis to Train Emotion Classifiers | Nowadays, the automatic detection of emotions is employed by many
applications in different fields like security informatics, e-learning, humor
detection, targeted advertising, etc. Many of these applications focus on
social media and treat this problem as a classification problem, which requires
preparing training dat... | ['Mohammed Al-Kabi', 'Mahmoud Al-Ayyoub', 'Wegdan Hussien', 'Yahya Tashtoush'] | 2019-02-24 | null | null | null | null | ['humor-detection'] | ['natural-language-processing'] | [ 2.05173880e-01 3.15091282e-01 -1.48884077e-02 -4.53984261e-01
-3.92337352e-01 -4.59968209e-01 5.04691899e-01 7.58403897e-01
-8.05705488e-01 9.73212481e-01 -3.70190620e-01 -3.83898407e-01
1.44232109e-01 -9.62664306e-01 -4.01227057e-01 -4.73564774e-01
3.91399264e-02 6.83237135e-01 3.10636461e-01 -3.74113321... | [10.244097709655762, 8.927212715148926] |
b4e9c293-9123-4590-915b-5a4e33ddc3ba | classification-of-complex-systems-based-on | 2008.13503 | null | https://arxiv.org/abs/2008.13503v1 | https://arxiv.org/pdf/2008.13503v1.pdf | Classification of Complex Systems Based on Transients | In order to develop systems capable of modeling artificial life, we need to identify, which systems can produce complex behavior. We present a novel classification method applicable to any class of deterministic discrete space and time dynamical systems. The method distinguishes between different asymptotic behaviors o... | ['Barbora Hudcova', 'Tomas Mikolov'] | 2020-08-31 | null | null | null | null | ['artificial-life'] | ['miscellaneous'] | [ 1.29501522e-01 -2.61950254e-01 3.48458081e-01 3.66684198e-01
2.67598629e-01 -1.00419378e+00 9.75003004e-01 2.21486241e-01
-2.63651520e-01 9.20512319e-01 -2.89506763e-01 -7.41471529e-01
3.59781906e-02 -1.10757864e+00 -1.93624347e-01 -8.91629398e-01
-3.17082435e-01 4.82550591e-01 6.22274458e-01 -7.26546288... | [5.593572616577148, 4.19366979598999] |
16b6de23-0d44-4b89-aca7-81fa9e2904b7 | pvt-a-simple-end-to-end-latency-aware-visual | 2211.11629 | null | https://arxiv.org/abs/2211.11629v2 | https://arxiv.org/pdf/2211.11629v2.pdf | PVT++: A Simple End-to-End Latency-Aware Visual Tracking Framework | Visual object tracking is essential to intelligent robots. Most existing approaches have ignored the online latency that can cause severe performance degradation during real-world processing. Especially for unmanned aerial vehicles (UAVs), where robust tracking is more challenging and onboard computation is limited, th... | ['Changhong Fu', 'Hang Zhao', 'Sebastian Scherer', 'Yiming Li', 'Junjie Ye', 'Ziyuan Huang', 'Bowen Li'] | 2022-11-21 | null | null | null | null | ['visual-tracking', 'visual-object-tracking'] | ['computer-vision', 'computer-vision'] | [-1.01039596e-01 -5.02500474e-01 -2.82444894e-01 1.52993947e-01
-3.83936286e-01 -9.35175538e-01 4.30987120e-01 -7.72586092e-02
-4.33355033e-01 2.89765924e-01 -3.26398820e-01 -4.14385825e-01
-1.54013738e-01 -1.56207964e-01 -7.49648809e-01 -4.91850227e-01
-3.71935487e-01 4.36685570e-02 7.07919300e-01 3.71719562... | [6.396407127380371, -2.1103551387786865] |
3425c567-27f1-46fd-9019-7b0cb03a71b5 | deep-neural-networks-for-the-sequential | 2006.05587 | null | https://arxiv.org/abs/2006.05587v3 | https://arxiv.org/pdf/2006.05587v3.pdf | Sequential Density Ratio Estimation for Simultaneous Optimization of Speed and Accuracy | Classifying sequential data as early and as accurately as possible is a challenging yet critical problem, especially when a sampling cost is high. One algorithm that achieves this goal is the sequential probability ratio test (SPRT), which is known as Bayes-optimal: it can keep the expected number of data samples as sm... | ['Taiki Miyagawa', 'Akinori F. Ebihara', 'Kazuyuki Sakurai', 'Hitoshi Imaoka'] | 2020-06-10 | sequential-density-ratio-estimation-for | https://openreview.net/forum?id=Rhsu5qD36cL | https://openreview.net/pdf?id=Rhsu5qD36cL | iclr-2021-1 | ['density-ratio-estimation'] | ['methodology'] | [ 1.40145257e-01 -3.74720216e-01 -4.49905127e-01 -4.12145823e-01
-1.12761486e+00 -3.10021430e-01 2.57082164e-01 1.70075625e-01
-7.10481942e-01 8.88110757e-01 -4.91094649e-01 -5.10140717e-01
-3.97293478e-01 -7.51435161e-01 -9.01126444e-01 -6.60646379e-01
-2.65790880e-01 5.88971674e-01 3.97265255e-01 4.14547354... | [8.499262809753418, 3.956108570098877] |
58bc52e4-86f7-47a1-a04c-8125e2c6ba10 | classical-versus-quantum-comparing-tensor | 2202.10471 | null | https://arxiv.org/abs/2202.10471v2 | https://arxiv.org/pdf/2202.10471v2.pdf | Classical versus Quantum: comparing Tensor Network-based Quantum Circuits on LHC data | Tensor Networks (TN) are approximations of high-dimensional tensors designed to represent locally entangled quantum many-body systems efficiently. This study provides a comprehensive comparison between classical TNs and TN-inspired quantum circuits in the context of Machine Learning on highly complex, simulated LHC dat... | ['Michael Spannowsky', 'Jack Y. Araz'] | 2022-02-21 | null | null | null | null | ['tensor-networks'] | ['methodology'] | [ 6.76002959e-03 2.35748991e-01 2.53274471e-01 -1.50947049e-01
-8.21398973e-01 -5.32577157e-01 6.43371940e-01 -5.26136607e-02
-5.38080812e-01 7.04447210e-01 2.86620911e-02 -4.41419303e-01
-4.13858742e-01 -1.00907302e+00 -5.55900753e-01 -9.89902139e-01
-3.43827516e-01 7.82806516e-01 7.26118684e-02 -6.70825660... | [5.638866901397705, 4.9553542137146] |
4c0e612e-7757-4e51-bd46-10f11989d8ba | ranking-job-offers-for-candidates-learning | null | null | https://aclanthology.org/L14-1615 | https://aclanthology.org/L14-1615.pdf | Ranking Job Offers for Candidates: learning hidden knowledge from Big Data | This paper presents a system for suggesting a ranked list of appropriate vacancy descriptions to job seekers in a job board web site. In particular our work has explored the use of supervised classifiers with the objective of learning implicit relations which cannot be found with similarity or pattern based search meth... | ["Felipe Nav{\\'\\i}o", "N{\\'u}ria Bel", 'Marc Poch', 'Sergio Espeja'] | 2014-05-01 | null | null | null | lrec-2014-5 | ['implicit-relations'] | ['natural-language-processing'] | [ 2.99384389e-02 2.54296690e-01 -5.67269862e-01 -6.51558280e-01
-2.08847731e-01 -3.58324826e-01 8.93459439e-01 7.32501745e-01
-6.33708954e-01 1.16745341e+00 1.91583171e-01 -6.30788028e-01
-1.00191283e+00 -7.90119708e-01 1.97631702e-01 -3.94559562e-01
1.31679296e-01 1.23349309e+00 3.26986462e-01 -4.30243582... | [9.940918922424316, 8.715893745422363] |
4a91257c-4b01-44a6-93fe-9523f59045ef | rubik-s-optical-neural-networks-multi-task | 2304.12985 | null | https://arxiv.org/abs/2304.12985v2 | https://arxiv.org/pdf/2304.12985v2.pdf | Rubik's Optical Neural Networks: Multi-task Learning with Physics-aware Rotation Architecture | Recently, there are increasing efforts on advancing optical neural networks (ONNs), which bring significant advantages for machine learning (ML) in terms of power efficiency, parallelism, and computational speed. With the considerable benefits in computation speed and energy efficiency, there are significant interests ... | ['Cunxi Yu', 'Weilu Gao', 'Yingjie Li'] | 2023-04-25 | null | null | null | null | ['rubik-s-cube'] | ['graphs'] | [ 3.85726541e-01 -1.27644867e-01 -1.31928800e-02 -1.94618642e-01
-2.54281163e-01 -1.93880871e-01 1.93763539e-01 -3.24416101e-01
-6.39684319e-01 7.67770290e-01 -4.43671167e-01 -3.68822455e-01
-3.37528139e-01 -5.25692344e-01 -9.37803090e-01 -1.08547974e+00
5.72715439e-02 1.64135695e-01 1.90473139e-01 1.96217999... | [8.289546966552734, 2.511937141418457] |
0d0b7365-2f71-4e84-a1e7-51346b2fe4c7 | progressive-with-purpose-guiding-progressive | 2209.10071 | null | https://arxiv.org/abs/2209.10071v2 | https://arxiv.org/pdf/2209.10071v2.pdf | Progressive with Purpose: Guiding Progressive Inpainting DNNs through Context and Structure | The advent of deep learning in the past decade has significantly helped advance image inpainting. Although achieving promising performance, deep learning-based inpainting algorithms still struggle from the distortion caused by the fusion of structural and contextual features, which are commonly obtained from, respectiv... | ['Jun Chen', 'Muhammad Alrabeiah', 'Kangdi Shi'] | 2022-09-21 | null | null | null | null | ['image-inpainting'] | ['computer-vision'] | [ 4.34363514e-01 -1.86699420e-01 -1.49147168e-01 -1.10032909e-01
-8.02782893e-01 -6.37564585e-02 2.55807281e-01 1.05071813e-01
-2.96183228e-01 7.14048564e-01 3.88474733e-01 2.67162651e-01
-8.76486078e-02 -7.81845748e-01 -8.74238968e-01 -8.46890688e-01
-7.65684701e-04 -2.77685463e-01 -1.72949225e-01 -1.31922722... | [11.266134262084961, -1.7344385385513306] |
2a7a5cca-5a00-47c8-964d-09da343fa816 | evolution-in-groups-a-deeper-look-at-synaptic | 1704.02081 | null | http://arxiv.org/abs/1704.02081v1 | http://arxiv.org/pdf/1704.02081v1.pdf | Evolution in Groups: A deeper look at synaptic cluster driven evolution of deep neural networks | A promising paradigm for achieving highly efficient deep neural networks is
the idea of evolutionary deep intelligence, which mimics biological evolution
processes to progressively synthesize more efficient networks. A crucial design
factor in evolutionary deep intelligence is the genetic encoding scheme used to
simula... | ['Elnaz Barshan', 'Alexander Wong', 'Mohammad Javad Shafiee'] | 2017-04-07 | null | null | null | null | ['object-categorization'] | ['computer-vision'] | [ 3.02667730e-03 -2.10673466e-01 6.29132867e-01 -3.31194758e-01
6.99427187e-01 -1.63362905e-01 2.03341410e-01 -6.26941770e-02
-7.09611475e-01 6.66800320e-01 -5.61223388e-01 -1.73832789e-01
-3.11433554e-01 -1.34077132e+00 -7.90330291e-01 -9.22781587e-01
-1.65289596e-01 4.74917710e-01 4.51516032e-01 -3.54656219... | [8.370542526245117, 3.1074445247650146] |
fe689b32-9c37-4e17-aaef-704512185737 | hih-towards-more-accurate-face-alignment-via | 2104.03100 | null | https://arxiv.org/abs/2104.03100v2 | https://arxiv.org/pdf/2104.03100v2.pdf | HIH: Towards More Accurate Face Alignment via Heatmap in Heatmap | Heatmap-based regression overcomes the lack of spatial and contextual information of direct coordinate regression, and has revolutionized the task of face alignment. Yet it suffers from quantization errors caused by neglecting subpixel coordinates in image resizing and network downsampling. In this paper, we first quan... | ['Jian Cheng', 'Jian Xue', 'Qiang Chen', 'Qinghao Hu', 'Xing Lan'] | 2021-04-07 | null | null | null | null | ['face-alignment'] | ['computer-vision'] | [ 1.40415117e-01 2.40250677e-01 -4.27985340e-01 -6.87063754e-01
-6.41537130e-01 -2.39565447e-01 3.87261182e-01 -9.03812423e-02
-1.20356366e-01 7.75251806e-01 1.22857420e-02 -8.86511579e-02
-3.98829617e-02 -6.84565008e-01 -9.34095085e-01 -9.03282285e-01
2.90991217e-01 2.83259124e-01 -1.45340890e-01 -9.26591828... | [13.452207565307617, 0.49345308542251587] |
094377cd-ee72-4469-82d2-725e18d0a396 | learning-to-compose-soft-prompts-for | 2204.03574 | null | https://arxiv.org/abs/2204.03574v3 | https://arxiv.org/pdf/2204.03574v3.pdf | Learning to Compose Soft Prompts for Compositional Zero-Shot Learning | We introduce compositional soft prompting (CSP), a parameter-efficient learning technique to improve the zero-shot compositionality of large-scale pretrained vision-language models (VLMs) like CLIP. We develop CSP for compositional zero-shot learning, the task of predicting unseen attribute-object compositions (e.g., o... | ['Stephen H. Bach', 'Peilin Yu', 'Nihal V. Nayak'] | 2022-04-07 | null | null | null | null | ['compositional-zero-shot-learning'] | ['computer-vision'] | [ 4.37062830e-01 3.02417427e-02 -2.53883898e-01 -5.24156034e-01
-9.40909386e-01 -8.54879200e-01 7.98944175e-01 1.53210852e-02
-6.20146275e-01 4.76040900e-01 1.90101400e-01 4.49047796e-02
4.02029991e-01 -5.88735819e-01 -1.07265759e+00 -6.57768965e-01
3.25069539e-02 7.01757014e-01 5.00744760e-01 -7.06602708... | [10.093873977661133, 2.0935192108154297] |
0d501fe3-4704-42d6-8276-cc5dbfa72a9c | graph-sampling-for-node-embedding | 2210.10520 | null | https://arxiv.org/abs/2210.10520v1 | https://arxiv.org/pdf/2210.10520v1.pdf | Graph sampling for node embedding | Node embedding is a central topic in graph representation learning. Computational efficiency and scalability can be challenging to any method that requires full-graph operations. We propose sampling approaches to node embedding, with or without explicit modelling of the feature vector, which aim to extract useful infor... | ['Li-Chun Zhang'] | 2022-10-19 | null | null | null | null | ['graph-sampling'] | ['graphs'] | [ 1.92256600e-01 4.80084121e-01 -2.87922114e-01 1.44082820e-02
-2.26102263e-01 -3.55679274e-01 6.55862391e-01 5.39069176e-01
-3.36853117e-01 5.50864160e-01 4.76820804e-02 -1.90312132e-01
-4.19091046e-01 -8.96378398e-01 -3.15573186e-01 -6.66663527e-01
-7.06498444e-01 5.03278375e-01 -3.53518873e-02 -2.19097465... | [7.202200889587402, 6.027388095855713] |
a75660b0-0ae4-439b-9c80-b0bbb4663de2 | can-forward-gradient-match-backpropagation | 2306.06968 | null | https://arxiv.org/abs/2306.06968v1 | https://arxiv.org/pdf/2306.06968v1.pdf | Can Forward Gradient Match Backpropagation? | Forward Gradients - the idea of using directional derivatives in forward differentiation mode - have recently been shown to be utilizable for neural network training while avoiding problems generally associated with backpropagation gradient computation, such as locking and memorization requirements. The cost is the req... | ['Edouard Oyallon', 'Michael Eickenberg', 'Eugene Belilovsky', 'Stéphane Rivaud', 'Louis Fournier'] | 2023-06-12 | null | null | null | null | ['memorization'] | ['natural-language-processing'] | [ 2.31747925e-02 -3.18874627e-01 -1.88231871e-01 -4.01968002e-01
-4.31480587e-01 -3.32769394e-01 6.50692165e-01 -2.28142574e-01
-1.07741606e+00 1.06666756e+00 2.01636314e-01 -4.07703429e-01
-1.73336118e-01 -6.00106418e-01 -6.69325650e-01 -1.11815512e+00
-1.57166868e-01 2.66119003e-01 1.72521442e-01 -3.95442605... | [7.996252059936523, 3.505533218383789] |
706385c7-b524-4eb9-ad28-a831eb216e4b | computing-forward-reachable-sets-for | 2209.07780 | null | https://arxiv.org/abs/2209.07780v3 | https://arxiv.org/pdf/2209.07780v3.pdf | Computing Forward Reachable Sets for Nonlinear Adaptive Multirotor Controllers | In multirotor systems, guaranteeing safety while considering unknown disturbances is essential for robust trajectory planning. The Forward reachable set (FRS), the set of feasible states subject to bounded disturbances, can be utilized to identify robust and collision-free trajectories by checking the intersections wit... | ['Han-Lim Choi', 'Juyeop Han'] | 2022-09-16 | null | null | null | null | ['trajectory-planning'] | ['robots'] | [-2.59940296e-01 2.89331287e-01 4.10891138e-03 4.15590495e-01
-2.67279416e-01 -9.56002355e-01 3.57270002e-01 6.54173940e-02
-3.15943837e-01 9.75104690e-01 -3.79419297e-01 -7.20750988e-01
-6.41687989e-01 -5.21284580e-01 -7.49545634e-01 -7.44155645e-01
-2.40067378e-01 3.09522808e-01 3.17118078e-01 -6.62497997... | [5.157236576080322, 2.2069144248962402] |
aa05744c-24fb-4d3c-9dca-394d58acdbc8 | towards-personalized-healthcare-in-cardiac | 2207.05138 | null | https://arxiv.org/abs/2207.05138v1 | https://arxiv.org/pdf/2207.05138v1.pdf | Towards Personalized Healthcare in Cardiac Population: The Development of a Wearable ECG Monitoring System, an ECG Lossy Compression Schema, and a ResNet-Based AF Detector | Cardiovascular diseases (CVDs) are the number one cause of death worldwide. While there is growing evidence that the atrial fibrillation (AF) has strong associations with various CVDs, this heart arrhythmia is usually diagnosed using electrocardiography (ECG) which is a risk-free, non-intrusive, and cost-efficient tool... | ['Kwong-Sak Leung', 'Jia-Min Chen', 'Alex Pui-Wai Lee', 'Kam-Sang Woo', 'Yee Leung', 'Yu Zhou', 'Ya-Fen Chan', 'Sheung-Lai Lo', 'Peng-Fei Liu', 'Wei-Ying Yi'] | 2022-07-11 | null | null | null | null | ['electrocardiography-ecg'] | ['methodology'] | [ 3.23113799e-01 -5.60858063e-02 -5.20325825e-02 -3.01780194e-01
-5.06180167e-01 -3.21953624e-01 -2.49480113e-01 4.96524811e-01
-2.58268118e-01 7.22481787e-01 -4.67339791e-02 -3.87257397e-01
-4.58261758e-01 -7.91717529e-01 -1.81250319e-01 -5.96233785e-01
-4.94174570e-01 1.32454515e-01 -1.19577639e-01 3.51863861... | [14.200325965881348, 3.239351987838745] |
d6238a94-d27a-4039-ae18-004613c28a40 | elvis-empowering-locality-of-vision-language | 2304.05303 | null | https://arxiv.org/abs/2304.05303v1 | https://arxiv.org/pdf/2304.05303v1.pdf | ELVIS: Empowering Locality of Vision Language Pre-training with Intra-modal Similarity | Deep learning has shown great potential in assisting radiologists in reading chest X-ray (CXR) images, but its need for expensive annotations for improving performance prevents widespread clinical application. Visual language pre-training (VLP) can alleviate the burden and cost of annotation by leveraging routinely gen... | ['Thijs Kooi', 'Tae Soo Kim', 'Jaewoo Kang', 'Jaewoong Shin', 'Sumin Seo'] | 2023-04-11 | null | null | null | null | ['phrase-grounding'] | ['natural-language-processing'] | [ 1.63953677e-01 2.49157354e-01 -4.55484241e-01 -4.75741029e-01
-1.56967747e+00 -6.42711699e-01 3.80942553e-01 8.05835307e-01
-4.46755946e-01 4.37841117e-01 6.45072281e-01 -9.00904953e-01
-1.38126433e-01 -4.46220636e-01 -7.89918900e-01 -2.40524352e-01
5.15394919e-02 4.30952013e-01 3.30699652e-01 2.81069934... | [15.012418746948242, -1.7081348896026611] |
2fa86d8f-b8e4-4d74-a8ab-8a0e9f979163 | learning-in-context-learning-for-named-entity | 2305.11038 | null | https://arxiv.org/abs/2305.11038v3 | https://arxiv.org/pdf/2305.11038v3.pdf | Learning In-context Learning for Named Entity Recognition | Named entity recognition in real-world applications suffers from the diversity of entity types, the emergence of new entity types, and the lack of high-quality annotations. To address the above problems, this paper proposes an in-context learning-based NER approach, which can effectively inject in-context NER ability i... | ['Le Sun', 'Xianpei Han', 'Boxi Cao', 'Hua Wu', 'Dai Dai', 'Wei Jia', 'Jie Lou', 'Hongyu Lin', 'Yaojie Lu', 'Jiawei Chen'] | 2023-05-18 | null | null | null | null | ['few-shot-ner'] | ['natural-language-processing'] | [ 5.89476787e-02 3.48815918e-02 7.14247897e-02 -5.50242841e-01
-8.04707229e-01 -9.02536809e-01 2.39368081e-01 4.23073232e-01
-8.34551096e-01 8.24301124e-01 -2.38003686e-01 -4.86220896e-01
-3.83881964e-02 -1.16512346e+00 -1.01764166e+00 -2.49161705e-01
-1.28880329e-02 2.66948879e-01 4.34074312e-01 -3.50361407... | [9.610909461975098, 9.418034553527832] |
07c66a4a-88a8-4267-ae4f-69ac2d9f8204 | sap-ri-twitter-sentiment-analysis-in-two-days | null | null | https://aclanthology.org/S14-2091 | https://aclanthology.org/S14-2091.pdf | SAP-RI: Twitter Sentiment Analysis in Two Days | null | ['N', 'Nishta Malhotra', 'Daniel Dahlmeier', 'Akriti Vij', 'Naveen an'] | 2014-08-01 | null | null | null | semeval-2014-8 | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.426507472991943, 3.6914055347442627] |
f4e44e8f-1b9f-48cb-9dbb-0484b28bb8e0 | l1bsr-exploiting-detector-overlap-for-self | 2304.06871 | null | https://arxiv.org/abs/2304.06871v2 | https://arxiv.org/pdf/2304.06871v2.pdf | L1BSR: Exploiting Detector Overlap for Self-Supervised Single-Image Super-Resolution of Sentinel-2 L1B Imagery | High-resolution satellite imagery is a key element for many Earth monitoring applications. Satellites such as Sentinel-2 feature characteristics that are favorable for super-resolution algorithms such as aliasing and band-misalignment. Unfortunately the lack of reliable high-resolution (HR) ground truth limits the appl... | ['Gabriele Facciolo', 'Pablo Arias', 'Axel Davy', 'Jérémy Anger', 'Ngoc Long Nguyen'] | 2023-04-14 | null | null | null | null | ['image-super-resolution'] | ['computer-vision'] | [ 0.564355 -0.0841311 0.01462904 -0.6330556 -1.1841174 -0.52913815
0.5085201 -0.20496042 -0.45690164 0.9171426 -0.07367761 0.03661309
-0.3576116 -0.96834254 -0.86987007 -0.88887316 -0.4343573 -0.05391629
-0.10997073 -0.48050356 -0.24897654 0.833018 -1.4054841 0.3222974
0.8906466 1.1235878 0.1... | [9.938081741333008, -1.800282597541809] |
60f543a2-f880-4298-8e30-0850f922ab6a | enhancing-cross-lingual-natural-language-1 | 2305.12761 | null | https://arxiv.org/abs/2305.12761v1 | https://arxiv.org/pdf/2305.12761v1.pdf | Enhancing Cross-lingual Natural Language Inference by Soft Prompting with Multilingual Verbalizer | Cross-lingual natural language inference is a fundamental problem in cross-lingual language understanding. Many recent works have used prompt learning to address the lack of annotated parallel corpora in XNLI. However, these methods adopt discrete prompting by simply translating the templates to the target language and... | ['Lijie Wen', 'Philip S. Yu', 'Fukun Ma', 'Yawen Yang', 'Aiwei Liu', 'Xuming Hu', 'Shuang Li'] | 2023-05-22 | null | null | null | null | ['cross-lingual-natural-language-inference', 'cross-lingual-transfer'] | ['natural-language-processing', 'natural-language-processing'] | [-3.79654318e-02 1.03415614e-02 -4.18423921e-01 -7.59132326e-01
-1.29397011e+00 -8.57974231e-01 5.87631345e-01 -4.70071763e-01
-7.53447890e-01 6.58062994e-01 4.89896357e-01 -4.03262794e-01
1.03512608e-01 -5.27870536e-01 -8.45843732e-01 -3.74003023e-01
9.42329764e-01 8.18562806e-01 -7.70532861e-02 -2.41464600... | [10.991217613220215, 9.492179870605469] |
7b362d7f-b4d7-451e-a322-ae520da8dbf5 | molecular-de-novo-design-through-deep | 1704.07555 | null | http://arxiv.org/abs/1704.07555v2 | http://arxiv.org/pdf/1704.07555v2.pdf | Molecular De Novo Design through Deep Reinforcement Learning | This work introduces a method to tune a sequence-based generative model for
molecular de novo design that through augmented episodic likelihood can learn
to generate structures with certain specified desirable properties. We
demonstrate how this model can execute a range of tasks such as generating
analogues to a query... | ['Thomas Blaschke', 'Hongming Chen', 'Ola Engkvist', 'Marcus Olivecrona'] | 2017-04-25 | null | null | null | null | ['activity-prediction', 'activity-prediction'] | ['computer-vision', 'time-series'] | [ 9.18814361e-01 6.29260302e-01 -2.84145176e-01 -1.69624135e-01
-7.73419559e-01 -8.30976307e-01 7.86164582e-01 3.47629875e-01
-2.09821299e-01 1.70938432e+00 6.48171008e-02 -5.04285693e-01
2.11153805e-01 -8.97937059e-01 -9.18522418e-01 -8.57953608e-01
-1.13034256e-01 6.69169486e-01 6.79217950e-02 -1.91613793... | [4.928395748138428, 5.737380027770996] |
cb060785-3b30-435d-bcf8-ebd4ddf0eb3c | hopfield-model-with-planted-patterns-a | 2304.13710 | null | https://arxiv.org/abs/2304.13710v1 | https://arxiv.org/pdf/2304.13710v1.pdf | Hopfield model with planted patterns: a teacher-student self-supervised learning model | While Hopfield networks are known as paradigmatic models for memory storage and retrieval, modern artificial intelligence systems mainly stand on the machine learning paradigm. We show that it is possible to formulate a teacher-student self-supervised learning problem with Boltzmann machines in terms of a suitable gene... | ['Daniele Tantari', 'Gianluca Manzan', 'Luca Camanzi', 'Francesco Alemanno'] | 2023-04-26 | null | null | null | null | ['memorization'] | ['natural-language-processing'] | [ 3.73236448e-01 3.19564402e-01 -1.00352317e-01 -2.85153866e-01
2.98168778e-01 -1.85619682e-01 8.81397307e-01 2.46305913e-01
-9.56650734e-01 8.26695144e-01 -3.47940654e-01 3.24538834e-02
-5.86067915e-01 -1.07643187e+00 -5.87913454e-01 -1.42615223e+00
-3.37065533e-02 7.96787262e-01 5.96750855e-01 -3.11870456... | [7.932426929473877, 3.5184669494628906] |
7db4119b-a21a-4f72-a67c-4ebca86218e4 | licd-a-language-independent-approach-for | null | null | https://link.springer.com/chapter/10.1007/978-3-030-15712-8_37 | https://link.springer.com/chapter/10.1007/978-3-030-15712-8_37 | LICD: A Language-Independent Approach for Aspect Category Detection | Aspect-based sentiment analysis (ABSA) deals with processing and summarizing customer reviews and has been a topic of interest in recent years. Given a set of predefined categories, Aspect Category Detection (ACD), as a subtask of ABSA, aims to assign a subset of these categories to a given review sentence. Thanks to t... | ['Azadeh Shakery', 'Heshaam Faili', 'Masoud Jalili Sabet', 'Sajad Movahedi', 'Erfan Ghadery'] | 2019-04-07 | null | null | null | ecir-2019-4 | ['aspect-term-extraction-and-sentiment', 'text-matching', 'aspect-based-sentiment-analysis', 'semantic-textual-similarity', 'aspect-category-detection'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 4.51531112e-02 -3.64846349e-01 -1.44841567e-01 -7.42484510e-01
-7.17920303e-01 -5.44368684e-01 7.78325737e-01 7.45095730e-01
-5.47758460e-01 2.80626804e-01 2.36714169e-01 -4.57705230e-01
3.49075645e-02 -9.19742763e-01 -3.40670794e-01 -5.11960328e-01
3.66879910e-01 3.35269064e-01 4.03517485e-02 -4.77832049... | [11.314806938171387, 6.67863655090332] |
9009dc44-2f28-4d5d-969d-5eacae515b7a | araguaia-medical-vision-lab-at-isic-2017-skin | 1703.00856 | null | http://arxiv.org/abs/1703.00856v1 | http://arxiv.org/pdf/1703.00856v1.pdf | Araguaia Medical Vision Lab at ISIC 2017 Skin Lesion Classification Challenge | This paper describes the participation of Araguaia Medical Vision Lab at the
International Skin Imaging Collaboration 2017 Skin Lesion Challenge. We
describe the use of deep convolutional neural networks in attempt to classify
images of Melanoma and Seborrheic Keratosis lesions. With use of finetuned
GoogleNet and Alex... | ['Larissa Vasconcellos de Moraes', 'Rafael Teixeira Sousa'] | 2017-03-02 | null | null | null | null | ['skin-lesion-classification'] | ['medical'] | [ 6.68234453e-02 -1.14906073e-01 -3.18728030e-01 6.59072772e-02
-3.15195560e-01 -4.76579219e-01 8.93302858e-01 2.14681197e-02
-6.69580460e-01 6.68447793e-01 1.77488074e-01 -4.27460194e-01
-9.15172175e-02 -6.40193701e-01 -2.80168746e-02 -5.70170045e-01
1.60784557e-01 -3.36707413e-01 4.30462323e-02 -1.19014367... | [15.714887619018555, -3.0264394283294678] |
03064a15-0ecb-4a0d-aa71-e04a481e0c6e | meta-style-adversarial-training-for-cross | 2302.09309 | null | https://arxiv.org/abs/2302.09309v2 | https://arxiv.org/pdf/2302.09309v2.pdf | StyleAdv: Meta Style Adversarial Training for Cross-Domain Few-Shot Learning | Cross-Domain Few-Shot Learning (CD-FSL) is a recently emerging task that tackles few-shot learning across different domains. It aims at transferring prior knowledge learned on the source dataset to novel target datasets. The CD-FSL task is especially challenged by the huge domain gap between different datasets. Critica... | ['Yu-Gang Jiang', 'Yanwei Fu', 'Yu Xie', 'Yuqian Fu'] | 2023-02-18 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Fu_StyleAdv_Meta_Style_Adversarial_Training_for_Cross-Domain_Few-Shot_Learning_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Fu_StyleAdv_Meta_Style_Adversarial_Training_for_Cross-Domain_Few-Shot_Learning_CVPR_2023_paper.pdf | cvpr-2023-1 | ['cross-domain-few-shot', 'cross-domain-few-shot-learning'] | ['computer-vision', 'computer-vision'] | [ 3.55849236e-01 -1.87990487e-01 1.11944102e-01 -2.81679988e-01
-6.08846128e-01 -8.99944127e-01 6.62852705e-01 -5.45219004e-01
-2.59148002e-01 6.36859417e-01 -3.43808122e-02 -1.30523210e-02
3.01804483e-01 -8.58970106e-01 -9.52961385e-01 -6.03176892e-01
4.50852722e-01 3.44716251e-01 4.81213272e-01 -6.90076590... | [10.07422924041748, 2.6877658367156982] |
fba7d059-db31-4507-9144-5e11c786cbad | audiolm-a-language-modeling-approach-to-audio | 2209.03143 | null | https://arxiv.org/abs/2209.03143v1 | https://arxiv.org/pdf/2209.03143v1.pdf | AudioLM: a Language Modeling Approach to Audio Generation | We introduce AudioLM, a framework for high-quality audio generation with long-term consistency. AudioLM maps the input audio to a sequence of discrete tokens and casts audio generation as a language modeling task in this representation space. We show how existing audio tokenizers provide different trade-offs between re... | ['Neil Zeghidour', 'Marco Tagliasacchi', 'David Grangier', 'Olivier Teboul', 'Matt Sharifi', 'Olivier Pietquin', 'Eugene Kharitonov', 'Damien Vincent', 'Raphaël Marinier', 'Zalán Borsos'] | 2022-09-07 | null | null | null | null | ['audio-generation'] | ['audio'] | [ 2.38450065e-01 4.60263193e-01 2.53751129e-02 -3.08918685e-01
-1.65010130e+00 -8.65055382e-01 4.99178946e-01 -7.48133436e-02
1.54723912e-01 4.24331963e-01 7.70474315e-01 4.98030260e-02
3.18967015e-01 -4.29638863e-01 -8.36751878e-01 -2.88982183e-01
-2.90162712e-01 2.31758133e-01 -2.15916157e-01 3.69878625... | [15.584821701049805, 5.803701877593994] |
97307b7e-30fa-4817-b215-d2ad2c2b1939 | does-localization-inform-editing-surprising | 2301.04213 | null | https://arxiv.org/abs/2301.04213v1 | https://arxiv.org/pdf/2301.04213v1.pdf | Does Localization Inform Editing? Surprising Differences in Causality-Based Localization vs. Knowledge Editing in Language Models | Language models are known to learn a great quantity of factual information during pretraining, and recent work localizes this information to specific model weights like mid-layer MLP weights (Meng et al., 2022). In this paper, we find that we can change how a fact is stored in a model by editing weights that are in a d... | ['Asma Ghandeharioun', 'Been Kim', 'Mohit Bansal', 'Peter Hase'] | 2023-01-10 | null | null | null | null | ['model-editing'] | ['natural-language-processing'] | [ 2.51729459e-01 3.65152538e-01 -2.67555326e-01 -5.66293120e-01
-4.24552649e-01 -7.00647295e-01 7.73200154e-01 2.62804896e-01
-5.69978237e-01 7.79835522e-01 5.54939806e-01 -5.47255039e-01
-1.51433989e-01 -1.03323925e+00 -1.15918386e+00 -4.95766103e-01
2.53246278e-01 2.92554557e-01 3.31142731e-02 -1.20417312... | [9.993653297424316, 7.66853666305542] |
6aae7729-b384-40a6-bf43-e8dd257aa85b | two-stage-voice-anonymization-for-enhanced | 2306.16069 | null | https://arxiv.org/abs/2306.16069v1 | https://arxiv.org/pdf/2306.16069v1.pdf | Two-Stage Voice Anonymization for Enhanced Privacy | In recent years, the need for privacy preservation when manipulating or storing personal data, including speech , has become a major issue. In this paper, we present a system addressing the speaker-level anonymization problem. We propose and evaluate a two-stage anonymization pipeline exploiting a state-of-the-art anon... | ['Patrick A. Naylor', 'Joerg Bitzer', 'Daniel Barreda', 'Francesco Nespoli'] | 2023-06-28 | null | null | null | null | ['voice-conversion', 'voice-conversion'] | ['audio', 'speech'] | [ 1.49333164e-01 2.70624489e-01 1.72789708e-01 -6.84480429e-01
-1.16888535e+00 -9.79566038e-01 4.09920931e-01 2.94375211e-01
-5.58132648e-01 4.49323922e-01 8.36359680e-01 -9.41244960e-02
4.03543524e-02 -3.19690198e-01 -4.37713861e-01 -1.15496002e-01
-6.90621361e-02 1.29089862e-01 -1.63119957e-01 -3.07201631... | [13.99368953704834, 5.883128643035889] |
a7906667-665a-4901-b226-44c5be81d363 | video-referring-expression-comprehension-via | 2210.02953 | null | https://arxiv.org/abs/2210.02953v1 | https://arxiv.org/pdf/2210.02953v1.pdf | Video Referring Expression Comprehension via Transformer with Content-aware Query | Video Referring Expression Comprehension (REC) aims to localize a target object in video frames referred by the natural language expression. Recently, the Transformerbased methods have greatly boosted the performance limit. However, we argue that the current query design is suboptima and suffers from two drawbacks: 1) ... | ['Yuexian Zou', 'Tengtao Song', 'Meng Cao', 'Ji Jiang'] | 2022-10-06 | null | null | null | null | ['referring-expression'] | ['computer-vision'] | [-6.47385791e-02 -1.10991649e-01 -3.06063920e-01 -4.35715586e-01
-1.21508729e+00 -4.62141126e-01 4.56734449e-01 -1.32262021e-01
-4.47096020e-01 5.66273987e-01 5.61600804e-01 1.39467254e-01
1.45964593e-01 -4.39658195e-01 -7.29516149e-01 -7.11213768e-01
3.13864976e-01 1.20624006e-02 2.98129350e-01 -1.85556531... | [10.278319358825684, 1.133104920387268] |
e77c0d60-b57a-4856-ae8b-930a317b6a71 | polyglot-semantic-role-labeling | 1805.11598 | null | http://arxiv.org/abs/1805.11598v1 | http://arxiv.org/pdf/1805.11598v1.pdf | Polyglot Semantic Role Labeling | Previous approaches to multilingual semantic dependency parsing treat
languages independently, without exploiting the similarities between semantic
structures across languages. We experiment with a new approach where we combine
resources from a pair of languages in the CoNLL 2009 shared task to build a
polyglot semanti... | ['Noah Smith', 'Swabha Swayamdipta', 'Phoebe Mulcaire'] | 2018-05-29 | polyglot-semantic-role-labeling-1 | https://aclanthology.org/P18-2106 | https://aclanthology.org/P18-2106.pdf | acl-2018-7 | ['semantic-dependency-parsing'] | ['natural-language-processing'] | [-3.39213423e-02 1.09878935e-01 -4.01392400e-01 -5.10179818e-01
-9.76854861e-01 -1.16828227e+00 8.86886477e-01 2.82124907e-01
-7.13700175e-01 9.75440979e-01 7.43934989e-01 -4.65478599e-01
7.84500316e-02 -1.33717448e-01 -4.11576390e-01 -2.38171518e-01
3.80982727e-01 5.84928334e-01 4.38024253e-01 -4.53443557... | [10.50412654876709, 9.651293754577637] |
a9f2ec2c-8266-4e57-aecb-81112c7dd647 | gaudi-a-neural-architect-for-immersive-3d | 2207.13751 | null | https://arxiv.org/abs/2207.13751v1 | https://arxiv.org/pdf/2207.13751v1.pdf | GAUDI: A Neural Architect for Immersive 3D Scene Generation | We introduce GAUDI, a generative model capable of capturing the distribution of complex and realistic 3D scenes that can be rendered immersively from a moving camera. We tackle this challenging problem with a scalable yet powerful approach, where we first optimize a latent representation that disentangles radiance fiel... | ['Josh Susskind', 'Afshin Dehghan', 'Daniel Ulbricht', 'Hanlin Goh', 'Shuangfei Zhai', 'Laurent Dinh', 'Zhuoyuan Chen', 'Alexander Toshev', 'Walter Talbott', 'Samira Abnar', 'Pengsheng Guo', 'Miguel Angel Bautista'] | 2022-07-27 | null | null | null | null | ['scene-generation'] | ['computer-vision'] | [ 1.92258447e-01 -2.67720282e-01 3.04864228e-01 -4.52658027e-01
-1.00230968e+00 -8.76833081e-01 9.47950721e-01 -7.31874108e-01
9.07794908e-02 4.98859555e-01 4.38312769e-01 -1.13009028e-01
5.57847209e-02 -8.29356015e-01 -1.09365189e+00 -1.05802190e+00
5.22821769e-02 5.56688309e-01 -1.89811558e-01 2.75232136... | [9.257680892944336, -3.109995126724243] |
c9bb1bd9-b9c9-461e-93e9-c8afb07a9cf7 | clip2video-mastering-video-text-retrieval-via | 2106.11097 | null | https://arxiv.org/abs/2106.11097v1 | https://arxiv.org/pdf/2106.11097v1.pdf | CLIP2Video: Mastering Video-Text Retrieval via Image CLIP | We present CLIP2Video network to transfer the image-language pre-training model to video-text retrieval in an end-to-end manner. Leading approaches in the domain of video-and-language learning try to distill the spatio-temporal video features and multi-modal interaction between videos and languages from a large-scale v... | ['Yu Chen', 'Luhui Xu', 'Pengfei Xiong', 'Han Fang'] | 2021-06-21 | null | null | null | null | ['video-text-retrieval'] | ['computer-vision'] | [-9.82778426e-03 -9.59305406e-01 -6.80389047e-01 -3.51476908e-01
-1.06940305e+00 -6.87914729e-01 9.22805965e-01 -1.86901063e-01
-7.35371530e-01 8.64827856e-02 4.73606199e-01 -7.67564178e-02
1.32699773e-01 -2.11879864e-01 -9.21305597e-01 -4.79616314e-01
-1.20634422e-01 3.09332550e-01 2.59018421e-01 -1.76599212... | [10.339366912841797, 0.9456770420074463] |
7c216eb9-4dde-4e4f-ab00-ce80aa588dd3 | on-pursuit-of-designing-multi-modal | 2109.06085 | null | https://arxiv.org/abs/2109.06085v2 | https://arxiv.org/pdf/2109.06085v2.pdf | On Pursuit of Designing Multi-modal Transformer for Video Grounding | Video grounding aims to localize the temporal segment corresponding to a sentence query from an untrimmed video. Almost all existing video grounding methods fall into two frameworks: 1) Top-down model: It predefines a set of segment candidates and then conducts segment classification and regression. 2) Bottom-up model:... | ['Yuexian Zou', 'Can Zhang', 'Mike Zheng Shou', 'Long Chen', 'Meng Cao'] | 2021-09-13 | null | https://aclanthology.org/2021.emnlp-main.773 | https://aclanthology.org/2021.emnlp-main.773.pdf | emnlp-2021-11 | ['video-grounding'] | ['computer-vision'] | [ 3.16027910e-01 -8.07389617e-02 -5.66277504e-01 -5.15270710e-01
-1.26784825e+00 -2.65681863e-01 4.06293958e-01 -1.26161858e-01
-3.08584124e-01 3.62737745e-01 3.93749505e-01 -2.10691974e-01
3.84305894e-01 -6.34531140e-01 -1.22449231e+00 -5.52769899e-01
2.89360523e-01 2.00777620e-01 3.96326244e-01 -4.66086715... | [10.148066520690918, 0.7188199758529663] |
c69fe40e-f5c8-4ea6-8c5e-350a0b5d47fd | maps-a-noise-robust-progressive-learning | 2302.04589 | null | https://arxiv.org/abs/2302.04589v1 | https://arxiv.org/pdf/2302.04589v1.pdf | MAPS: A Noise-Robust Progressive Learning Approach for Source-Free Domain Adaptive Keypoint Detection | Existing cross-domain keypoint detection methods always require accessing the source data during adaptation, which may violate the data privacy law and pose serious security concerns. Instead, this paper considers a realistic problem setting called source-free domain adaptive keypoint detection, where only the well-tra... | ['Ran He', 'Aihua Zheng', 'Bo Jiang', 'Jian Liang', 'Yuhe Ding'] | 2023-02-09 | null | null | null | null | ['keypoint-detection'] | ['computer-vision'] | [ 3.01784933e-01 -6.86220219e-03 -7.46865749e-01 -3.96199733e-01
-1.16747093e+00 -8.36317897e-01 6.45494163e-01 2.96631306e-01
-5.19387841e-01 7.72350490e-01 1.08050175e-01 -2.22860113e-01
1.85102969e-01 -4.52938706e-01 -7.08619237e-01 -7.55451322e-01
1.87349349e-01 2.98476219e-01 3.49375606e-01 -7.64760822... | [10.393665313720703, 3.1871140003204346] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.