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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
575d827a-4a60-4461-8320-92d36e6424d4 | neural-knowledge-extraction-from-cloud | 2007.05505 | null | https://arxiv.org/abs/2007.05505v4 | https://arxiv.org/pdf/2007.05505v4.pdf | Neural Knowledge Extraction From Cloud Service Incidents | In the last decade, two paradigm shifts have reshaped the software industry - the move from boxed products to services and the widespread adoption of cloud computing. This has had a huge impact on the software development life cycle and the DevOps processes. Particularly, incident management has become critical for dev... | ['Sumit Kumar', 'Thomas Zimmermann', 'Manish Shetty', 'Chetan Bansal', 'Nikitha Rao', 'Nachiappan Nagappan'] | 2020-07-10 | null | null | null | null | ['entity-extraction'] | ['natural-language-processing'] | [-3.70867155e-03 -7.73741072e-03 -1.02000557e-01 -4.87692982e-01
-1.06499231e+00 -4.68818158e-01 3.01604182e-01 4.62964863e-01
-5.32091677e-01 3.58913451e-01 4.92032409e-01 -5.90303600e-01
9.86452252e-02 -7.57266045e-01 -4.89943862e-01 -1.44489482e-01
-5.25742099e-02 7.01616108e-01 -7.78430374e-04 -1.19007811... | [9.663893699645996, 9.371321678161621] |
612e842a-abd3-4a40-8f96-c902a6c55258 | context-aware-adaptive-and-scalable-android | 1706.00947 | null | http://arxiv.org/abs/1706.00947v2 | http://arxiv.org/pdf/1706.00947v2.pdf | Context-aware, Adaptive and Scalable Android Malware Detection through Online Learning (extended version) | It is well-known that Android malware constantly evolves so as to evade
detection. This causes the entire malware population to be non-stationary.
Contrary to this fact, most of the prior works on Machine Learning based
Android malware detection have assumed that the distribution of the observed
malware characteristics... | ['Mahinthan Chandramohan', 'Annamalai Narayanan', 'Yang Liu', 'Lihui Chen'] | 2017-06-03 | null | null | null | null | ['android-malware-detection'] | ['miscellaneous'] | [ 1.28615618e-01 -4.42562103e-01 -7.12037563e-01 8.95344540e-02
-3.56886685e-01 -6.07195735e-01 5.18410146e-01 3.10177326e-01
-1.56494007e-01 4.92060781e-01 -4.62153912e-01 -6.38492942e-01
-3.19489948e-02 -6.25230074e-01 -7.03617156e-01 -5.59697628e-01
-6.57556117e-01 2.74088621e-01 6.76390707e-01 8.23291987... | [14.422598838806152, 9.680986404418945] |
fb8846a2-502d-4e67-baf5-d0b72038c9b9 | entropic-causal-inference-identifiability-and-1 | 2101.03501 | null | https://arxiv.org/abs/2101.03501v1 | https://arxiv.org/pdf/2101.03501v1.pdf | Entropic Causal Inference: Identifiability and Finite Sample Results | Entropic causal inference is a framework for inferring the causal direction between two categorical variables from observational data. The central assumption is that the amount of unobserved randomness in the system is not too large. This unobserved randomness is measured by the entropy of the exogenous variable in the... | ['Dmitriy Katz', 'Kristjan Greenewald', 'Murat Kocaoglu', 'Spencer Compton'] | 2021-01-10 | entropic-causal-inference-identifiability-and | http://proceedings.neurips.cc/paper/2020/hash/a979ca2444b34449a2c80b012749e9cd-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/a979ca2444b34449a2c80b012749e9cd-Paper.pdf | neurips-2020-12 | ['causal-identification'] | ['reasoning'] | [ 2.17111096e-01 3.16084743e-01 -4.87397194e-01 2.89480891e-02
-3.23161125e-01 -7.44854569e-01 7.28353083e-01 1.18644997e-01
3.25860195e-02 1.06474078e+00 4.66823846e-01 -5.87880373e-01
-5.83223820e-01 -8.28059018e-01 -9.61872399e-01 -9.13289368e-01
-5.29232919e-01 4.77237940e-01 -1.95988610e-01 1.74559921... | [7.892215728759766, 5.330418109893799] |
0e238e2c-c0fc-4d39-84d3-5f4b62558335 | gsv-cities-toward-appropriate-supervised | 2210.10239 | null | https://arxiv.org/abs/2210.10239v1 | https://arxiv.org/pdf/2210.10239v1.pdf | GSV-Cities: Toward Appropriate Supervised Visual Place Recognition | This paper aims to investigate representation learning for large scale visual place recognition, which consists of determining the location depicted in a query image by referring to a database of reference images. This is a challenging task due to the large-scale environmental changes that can occur over time (i.e., we... | ['Philippe Giguère', 'Brahim Chaib-Draa', 'Amar Ali-bey'] | 2022-10-19 | null | null | null | null | ['visual-localization', 'visual-place-recognition'] | ['computer-vision', 'computer-vision'] | [-3.46330434e-01 -6.41688168e-01 -1.50980338e-01 -4.07311797e-01
-1.17542922e+00 -6.89099550e-01 8.01321387e-01 1.75468788e-01
-5.39561510e-01 8.43840599e-01 3.94288123e-01 -2.36751050e-01
7.84551725e-02 -1.00396287e+00 -8.80656183e-01 -4.33896452e-01
-1.95546091e-01 3.55824113e-01 1.04316600e-01 -2.11282104... | [7.748217582702637, -1.8542766571044922] |
59a9cb7b-bc97-40f6-9343-1ad04c0b63c1 | frequency-domain-multi-channel-acoustic | 1903.05299 | null | http://arxiv.org/abs/1903.05299v2 | http://arxiv.org/pdf/1903.05299v2.pdf | Frequency Domain Multi-channel Acoustic Modeling for Distant Speech Recognition | Conventional far-field automatic speech recognition (ASR) systems typically
employ microphone array techniques for speech enhancement in order to improve
robustness against noise or reverberation. However, such speech enhancement
techniques do not always yield ASR accuracy improvement because the
optimization criterion... | [] | 2019-04-28 | null | null | null | null | ['distant-speech-recognition'] | ['speech'] | [ 3.72330844e-01 -2.54678041e-01 7.69159794e-01 -3.17879170e-01
-1.15242279e+00 -2.73294836e-01 1.54411495e-01 -1.53790414e-01
-7.44363189e-01 4.45104301e-01 6.26078546e-01 -6.16190076e-01
-2.54681855e-02 -4.49933499e-01 -6.66665733e-01 -5.83277166e-01
-6.81596026e-02 -5.01568377e-01 -5.12565672e-02 -3.74892890... | [14.948020935058594, 5.952859878540039] |
a0dfb573-b5ca-4d45-bd2f-758c770fb381 | recasnet-improving-consistency-within-the-two | 2202.13912 | null | https://arxiv.org/abs/2202.13912v1 | https://arxiv.org/pdf/2202.13912v1.pdf | ReCasNet: Improving consistency within the two-stage mitosis detection framework | Mitotic count (MC) is an important histological parameter for cancer diagnosis and grading, but the manual process for obtaining MC from whole-slide histopathological images is very time-consuming and prone to error. Therefore, deep learning models have been proposed to facilitate this process. Existing approaches util... | ['Ekapol Chuangsuwanich', 'Sira Sriswasdi', 'Qingyi Tao', 'Shanop Shuangshoti', 'Sakun Santisukwongchote', 'Chawan Piansaddhayanon'] | 2022-02-28 | null | null | null | null | ['cell-detection', 'mitosis-detection'] | ['computer-vision', 'medical'] | [ 3.50055367e-01 -3.46954986e-02 -1.39934480e-01 -2.42256880e-01
-1.20031548e+00 -3.23938519e-01 4.45801198e-01 8.47267926e-01
-7.77670622e-01 4.62586969e-01 -2.13573486e-01 -4.20244873e-01
7.29003474e-02 -7.61250257e-01 -3.60364854e-01 -1.08704031e+00
1.81238919e-01 5.16255736e-01 5.61262131e-01 2.62660265... | [15.069877624511719, -3.096856117248535] |
03001b1e-4d62-407e-a903-2b6a91c5ae06 | blockwise-stochastic-variance-reduced-methods | 2305.18730 | null | https://arxiv.org/abs/2305.18730v2 | https://arxiv.org/pdf/2305.18730v2.pdf | Blockwise Stochastic Variance-Reduced Methods with Parallel Speedup for Multi-Block Bilevel Optimization | In this paper, we consider non-convex multi-block bilevel optimization (MBBO) problems, which involve $m\gg 1$ lower level problems and have important applications in machine learning. Designing a stochastic gradient and controlling its variance is more intricate due to the hierarchical sampling of blocks and data and ... | ['Tianbao Yang', 'Lijun Zhang', 'Zhishuai Guo', 'Zi-Hao Qiu', 'Quanqi Hu'] | 2023-05-30 | null | null | null | null | ['bilevel-optimization'] | ['methodology'] | [-2.78067142e-02 -2.43585765e-01 -5.97643405e-02 -7.96607360e-02
-9.57166374e-01 -3.38305414e-01 7.51859546e-02 2.75841653e-01
-6.57923162e-01 7.98761845e-01 -1.81862906e-01 -6.55000865e-01
-6.85651302e-01 -4.75600332e-01 -8.92073631e-01 -1.04111159e+00
-6.18944824e-01 2.58117765e-01 1.64993554e-01 -1.18845254... | [6.584084510803223, 4.501469135284424] |
650b0ddb-0a11-47f0-968a-a07a31108390 | submodular-minimax-optimization-finding | 2305.16903 | null | https://arxiv.org/abs/2305.16903v1 | https://arxiv.org/pdf/2305.16903v1.pdf | Submodular Minimax Optimization: Finding Effective Sets | Despite the rich existing literature about minimax optimization in continuous settings, only very partial results of this kind have been obtained for combinatorial settings. In this paper, we fill this gap by providing a characterization of submodular minimax optimization, the problem of finding a set (for either the m... | ['Amin Karbasi', 'Moran Feldman', 'Ethan R. Elenberg', 'Loay Mualem'] | 2023-05-26 | null | null | null | null | ['prompt-engineering'] | ['natural-language-processing'] | [ 2.04769686e-01 2.71777898e-01 -4.84599680e-01 -3.88032824e-01
-1.44308805e+00 -1.23526216e+00 3.63398582e-01 4.20583636e-01
-4.85550404e-01 7.70256519e-01 1.36568770e-01 -6.47050917e-01
-5.48209667e-01 -2.97597915e-01 -8.47003937e-01 -5.11566579e-01
1.89355090e-01 7.01332033e-01 6.34520724e-02 -3.53443831... | [6.453372478485107, 4.863454818725586] |
4622f240-35b6-41f7-b630-bac7719e7130 | dynamic-predictive-sampling-analog-to-digital | 2211.09901 | null | https://arxiv.org/abs/2211.09901v1 | https://arxiv.org/pdf/2211.09901v1.pdf | Dynamic Predictive Sampling Analog to Digital Converter for Sparse Signal Sensing | This paper presents a dynamic predictive sampling (DPS) based analog-to-digital converter (ADC) that provides a non-uniform sampling of input analog continuous-time signals. The processing unit generates a dynamic prediction of the input signal using two prior-quantized samplings to compute digital values of an upper t... | ['Wei Tang', 'Mario Renteria-Pinon', 'Xiaochen Tang'] | 2022-11-17 | null | null | null | null | ['data-compression'] | ['time-series'] | [ 9.96649265e-01 -2.68117547e-01 -3.98477733e-01 -4.92068857e-01
-5.04544079e-01 -4.28217262e-01 -1.56648345e-02 8.24172080e-01
-5.75488985e-01 7.56623089e-01 -1.08511217e-01 -2.90200740e-01
1.30614802e-01 -6.98612690e-01 3.08035733e-03 -3.51306021e-01
-9.02249739e-02 5.73092066e-02 7.29031265e-01 3.08743060... | [13.944103240966797, 3.157130241394043] |
a48ba6e0-5deb-4607-999d-08a028154cb3 | bert-got-a-date-introducing-transformers-to | 2109.14927 | null | https://arxiv.org/abs/2109.14927v3 | https://arxiv.org/pdf/2109.14927v3.pdf | BERT got a Date: Introducing Transformers to Temporal Tagging | Temporal expressions in text play a significant role in language understanding and correctly identifying them is fundamental to various retrieval and natural language processing systems. Previous works have slowly shifted from rule-based to neural architectures, capable of tagging expressions with higher accuracy. Howe... | ['Michael Gertz', 'Dennis Aumiller', 'Satya Almasian'] | 2021-09-30 | null | null | null | null | ['temporal-tagging'] | ['natural-language-processing'] | [ 1.78024173e-02 -7.48073608e-02 -6.47748947e-01 -5.34800470e-01
-7.74477720e-01 -8.49717200e-01 7.92940140e-01 2.63369262e-01
-6.19950712e-01 7.15098500e-01 2.14538679e-01 -3.91940296e-01
6.88094348e-02 -6.46962702e-01 -4.55841422e-01 -4.50470775e-01
-2.84145385e-01 5.14297903e-01 4.44871873e-01 -2.74167895... | [9.955606460571289, 9.28342342376709] |
a7e62bd8-9441-4bb8-8cf9-dd2b83557764 | combing-policy-evaluation-and-policy | 2109.11867 | null | https://arxiv.org/abs/2109.11867v2 | https://arxiv.org/pdf/2109.11867v2.pdf | The $f$-Divergence Reinforcement Learning Framework | The framework of deep reinforcement learning (DRL) provides a powerful and widely applicable mathematical formalization for sequential decision-making. This paper present a novel DRL framework, termed \emph{$f$-Divergence Reinforcement Learning (FRL)}. In FRL, the policy evaluation and policy improvement phases are sim... | ['Xianjie Zhang', 'Xinwen Hou', 'Xiaoyu Chen', 'Zhou Yang', 'Guoliang Fan', 'Yu Liu', 'Yunpeng Bai', 'Qiang He', 'Chen Gong'] | 2021-09-24 | null | null | null | null | ['mathematical-proofs'] | ['miscellaneous'] | [-3.09908390e-01 -4.78659049e-02 -4.47408170e-01 -7.43443817e-02
-7.31634080e-01 -5.75057328e-01 4.91091371e-01 7.27930441e-02
-9.56502914e-01 1.24993408e+00 -1.52140006e-01 -6.11097991e-01
-4.84619260e-01 -6.18023574e-01 -9.29937005e-01 -9.05249834e-01
-3.16288769e-01 1.55201584e-01 -2.32608095e-02 -2.24102139... | [4.156148433685303, 2.4112861156463623] |
ead2a45b-c55e-4405-894f-8b353a92653b | text2model-model-induction-for-zero-shot | 2210.15182 | null | https://arxiv.org/abs/2210.15182v1 | https://arxiv.org/pdf/2210.15182v1.pdf | Text2Model: Model Induction for Zero-shot Generalization Using Task Descriptions | We study the problem of generating a training-free task-dependent visual classifier from text descriptions without visual samples. This \textit{Text-to-Model} (T2M) problem is closely related to zero-shot learning, but unlike previous work, a T2M model infers a model tailored to a task, taking into account all classes ... | ['Gal Chechik', 'Roi Reichart', 'Eyal Ben-David', 'Tomer Volk', 'Ohad Amosy'] | 2022-10-27 | null | null | null | null | ['point-cloud-classification'] | ['computer-vision'] | [ 4.59946603e-01 4.38465685e-01 -4.89010721e-01 -6.98278844e-01
-5.77334344e-01 -1.97152406e-01 1.24570632e+00 -1.47858575e-01
-7.19109401e-02 2.76841879e-01 2.71914214e-01 -8.67253318e-02
-2.59934515e-01 -6.55302465e-01 -6.55234933e-01 -6.06210768e-01
2.56666601e-01 9.50106442e-01 3.05626512e-01 -4.01030369... | [10.104207038879395, 2.3220856189727783] |
7e623293-f71b-4b15-8adf-2a39d0de4f7d | the-cacapo-dataset-a-multilingual-multi | null | null | https://aclanthology.org/2020.inlg-1.10 | https://aclanthology.org/2020.inlg-1.10.pdf | The CACAPO Dataset: A Multilingual, Multi-Domain Dataset for Neural Pipeline and End-to-End Data-to-Text Generation | This paper describes the CACAPO dataset, built for training both neural pipeline and end-to-end data-to-text language generation systems. The dataset is multilingual (Dutch and English), and contains almost 10,000 sentences from human-written news texts in the sports, weather, stocks, and incidents domain, together wit... | ['Emiel Krahmer', 'Sander Wubben', 'Chris Emmery', 'Chris van der Lee'] | null | null | null | null | inlg-acl-2020-12 | ['data-to-text-generation'] | ['natural-language-processing'] | [-6.13773242e-02 3.46478879e-01 -4.16452289e-01 -7.42388606e-01
-1.09034979e+00 -8.63264680e-01 1.09310579e+00 2.03803003e-01
-6.39360070e-01 1.47332287e+00 1.12558067e+00 -1.89109549e-01
3.10382783e-01 -8.26550126e-01 -6.86061263e-01 -3.78371067e-02
5.95849566e-02 1.12120843e+00 -4.43031520e-01 -9.30705309... | [11.599140167236328, 9.542366027832031] |
ebe845e5-aefc-4ae9-8972-a340f8ed2672 | multi-view-improved-monitored-distillation | 2303.15840 | null | https://arxiv.org/abs/2303.15840v2 | https://arxiv.org/pdf/2303.15840v2.pdf | Enhancing Depth Completion with Multi-View Monitored Distillation | This paper presents a novel method for depth completion, which leverages multi-view improved monitored distillation to generate more precise depth maps. Our approach builds upon the state-of-the-art ensemble distillation method, in which we introduce a stereo-based model as a teacher model to improve the accuracy of th... | ['Hung-Chyun Chou', 'Ning Ding', 'Ming Ouyang', 'Chang-Zheng Zhang', 'Sen-Hua Zhu', 'Cong Li', 'Jia-Wei Guo'] | 2023-03-28 | null | null | null | null | ['depth-completion'] | ['computer-vision'] | [ 2.23174348e-01 4.66402858e-01 -1.50566861e-01 -4.43758637e-01
-1.13836253e+00 -4.46650207e-01 6.14431918e-01 -6.53092861e-02
-2.30087101e-01 5.94194293e-01 3.96060884e-01 -1.88507438e-01
1.64763719e-01 -9.25385773e-01 -7.59935379e-01 -6.32007420e-01
5.99541187e-01 5.24105489e-01 1.80559874e-01 -5.71220331... | [8.680563926696777, -2.821828842163086] |
3515365f-9f2e-451c-8ee0-d8f23c058305 | unsupervised-neural-aspect-search-with | 2005.02771 | null | https://arxiv.org/abs/2005.02771v1 | https://arxiv.org/pdf/2005.02771v1.pdf | Unsupervised Neural Aspect Search with Related Terms Extraction | The tasks of aspect identification and term extraction remain challenging in natural language processing. While supervised methods achieve high scores, it is hard to use them in real-world applications due to the lack of labelled datasets. Unsupervised approaches outperform these methods on several tasks, but it is sti... | ['Maria Khodorchenko', 'Timur Sokhin', 'Nikolay Butakov'] | 2020-05-06 | null | null | null | null | ['aspect-extraction'] | ['natural-language-processing'] | [ 2.88406372e-01 3.55561152e-02 -3.41339350e-01 -2.91514099e-01
-1.07122970e+00 -5.54658532e-01 7.10558832e-01 2.85922140e-01
-5.16891718e-01 3.62453222e-01 1.43688545e-01 -1.01281069e-01
-1.57510370e-01 -7.95308173e-01 -5.82155764e-01 -5.30981839e-01
1.21176355e-01 5.80911934e-01 2.41304152e-02 -2.04306513... | [11.476030349731445, 6.648472309112549] |
2ef13e31-1cb9-46f6-80d1-a235f4bbc365 | toward-neural-network-simulation-of | 2211.02929 | null | https://arxiv.org/abs/2211.02929v1 | https://arxiv.org/pdf/2211.02929v1.pdf | Toward Neural Network Simulation of Variational Quantum Algorithms | Variational quantum algorithms (VQAs) utilize a hybrid quantum-classical architecture to recast problems of high-dimensional linear algebra as ones of stochastic optimization. Despite the promise of leveraging near- to intermediate-term quantum resources to accelerate this task, the computational advantage of VQAs over... | ['Shravan Veerapaneni', 'James Stokes', 'Oliver Knitter'] | 2022-11-05 | null | null | null | null | ['neural-network-simulation', 'variational-monte-carlo'] | ['computer-code', 'miscellaneous'] | [ 8.01972598e-02 1.00745797e-01 3.81322920e-01 -1.44800857e-01
-1.03290880e+00 -5.82773328e-01 5.47560751e-01 -1.71133175e-01
-4.16355789e-01 9.10081625e-01 -2.02807151e-02 -5.58906376e-01
-2.95561135e-01 -1.00742698e+00 -5.82207322e-01 -1.11113071e+00
1.29921120e-02 7.04656482e-01 -1.97961152e-01 -6.50397897... | [5.570132255554199, 4.967688083648682] |
938fa5f1-a8fe-4d21-aa1e-7331dcf122fa | a-word-is-worth-a-thousand-dollars | null | null | https://openreview.net/forum?id=uFXjHTmvBph | https://openreview.net/pdf?id=uFXjHTmvBph | A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Meme Stock Prediction | More and more investors and machine learning models rely on social media (e.g., Twitter and Reddit) to gather information and predict certain stocks' prices (meme stock). However, text-based models are known to be vulnerable to adversarial attacks, but whether stock prediction models have similar adversarial vulnerabil... | ['Anonymous'] | 2021-10-16 | null | null | null | acl-arr-october-2021-10 | ['stock-prediction'] | ['time-series'] | [-3.66037011e-01 3.19407463e-01 4.98205656e-03 -1.11465275e-01
-5.06915689e-01 -1.24342716e+00 1.01945186e+00 6.07842579e-02
-2.43825480e-01 1.01216877e+00 2.10616663e-01 -5.14955819e-01
3.37518454e-01 -1.45788503e+00 -8.39641392e-01 -1.87922463e-01
-2.82670915e-01 5.63565493e-01 3.39146793e-01 -6.79858804... | [5.701230525970459, 7.6908650398254395] |
9450b9af-cb81-467a-b5bf-5c5926cb4557 | roca-robust-cad-model-retrieval-and-alignment | 2112.01988 | null | https://arxiv.org/abs/2112.01988v2 | https://arxiv.org/pdf/2112.01988v2.pdf | ROCA: Robust CAD Model Retrieval and Alignment from a Single Image | We present ROCA, a novel end-to-end approach that retrieves and aligns 3D CAD models from a shape database to a single input image. This enables 3D perception of an observed scene from a 2D RGB observation, characterized as a lightweight, compact, clean CAD representation. Core to our approach is our differentiable ali... | ['Matthias Nießner', 'Angela Dai', 'Can Gümeli'] | 2021-12-03 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Gumeli_ROCA_Robust_CAD_Model_Retrieval_and_Alignment_From_a_Single_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Gumeli_ROCA_Robust_CAD_Model_Retrieval_and_Alignment_From_a_Single_CVPR_2022_paper.pdf | cvpr-2022-1 | ['3d-dense-shape-correspondence', '3d-shape-reconstruction-from-a-single-2d', '3d-object-detection-from-monocular-images', '3d-object-retrieval'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 1.35953337e-01 -2.11263269e-01 1.15972934e-02 -5.90814888e-01
-1.43044353e+00 -9.15649891e-01 7.17216790e-01 -4.05215621e-02
-4.69888735e-04 -3.17415267e-01 -6.36983365e-02 2.89163347e-02
-8.24051052e-02 -7.38175154e-01 -9.38870311e-01 -6.25790209e-02
9.80528519e-02 1.07390153e+00 2.73196846e-02 -3.64204049... | [7.728906631469727, -2.718454122543335] |
58c26b12-1395-4704-b606-a736c3de6512 | attention-model-for-articulatory-features | 1907.01914 | null | https://arxiv.org/abs/1907.01914v1 | https://arxiv.org/pdf/1907.01914v1.pdf | Attention model for articulatory features detection | Articulatory distinctive features, as well as phonetic transcription, play important role in speech-related tasks: computer-assisted pronunciation training, text-to-speech conversion (TTS), studying speech production mechanisms, speech recognition for low-resourced languages. End-to-end approaches to speech-related tas... | ['Ievgen Karaulov', 'Dmytro Tkanov'] | 2019-07-02 | null | null | null | null | ['manner-of-articulation-detection'] | ['speech'] | [ 2.33319461e-01 -3.12472135e-01 -1.68313429e-01 -4.58247453e-01
-1.39325559e+00 -6.40646577e-01 6.97878957e-01 -3.23199481e-01
-5.37217677e-01 4.24283594e-01 6.14314556e-01 -7.04337895e-01
1.90136179e-01 1.55064687e-02 -6.72036231e-01 -3.46594214e-01
3.03676009e-01 7.37267017e-01 -1.18786938e-01 7.95469284... | [14.530191421508789, 6.87267541885376] |
c398fa13-d2c5-4ac2-be04-05ef2b963eac | discoman-dataset-of-indoor-scenes-for | 1909.12146 | null | https://arxiv.org/abs/1909.12146v1 | https://arxiv.org/pdf/1909.12146v1.pdf | DISCOMAN: Dataset of Indoor SCenes for Odometry, Mapping And Navigation | We present a novel dataset for training and benchmarking semantic SLAM methods. The dataset consists of 200 long sequences, each one containing 3000-5000 data frames. We generate the sequences using realistic home layouts. For that we sample trajectories that simulate motions of a simple home robot, and then render the... | ['Anton Konushin', 'Sergey Bykov', 'Konstantin Sofiiuk', 'Igor Slinko', 'Dmitry Zhukov', 'Pavel Kirsanov', 'Filipp Konokhov', 'Olga Barinova', 'Anna Vorontsova', 'Airat Gaskarov'] | 2019-09-26 | null | null | null | null | ['semantic-slam'] | ['computer-vision'] | [ 3.58739287e-01 -1.27444714e-01 3.03774595e-01 -5.19993126e-01
-7.89459288e-01 -4.17335808e-01 9.06060994e-01 7.52507001e-02
-4.63051081e-01 1.09556234e+00 8.00129846e-02 -5.56569844e-02
1.14063077e-01 -1.14421201e+00 -8.11177433e-01 -6.04513884e-01
-3.46919537e-01 1.28397644e+00 5.15075624e-01 -3.38448882... | [7.367717266082764, -2.1476480960845947] |
97bb9d52-e091-411b-aef7-35b3440b8704 | toward-real-world-single-image-deraining-a | 2206.05514 | null | https://arxiv.org/abs/2206.05514v2 | https://arxiv.org/pdf/2206.05514v2.pdf | Toward Real-world Single Image Deraining: A New Benchmark and Beyond | Single image deraining (SID) in real scenarios attracts increasing attention in recent years. Due to the difficulty in obtaining real-world rainy/clean image pairs, previous real datasets suffer from low-resolution images, homogeneous rain streaks, limited background variation, and even misalignment of image pairs, res... | ['DaCheng Tao', 'Xinmei Tian', 'Zhen Huang', 'Jing Zhang', 'Qiming Zhang', 'Wei Li'] | 2022-06-11 | null | null | null | null | ['single-image-deraining'] | ['computer-vision'] | [ 1.12001806e-01 -7.20730543e-01 3.05356652e-01 -5.29141963e-01
-8.34598839e-01 -4.25515652e-01 3.24483126e-01 -5.51691294e-01
-1.96494564e-01 1.13286757e+00 9.52200126e-03 -1.30451426e-01
-2.03636497e-01 -7.27823734e-01 -7.59631932e-01 -1.27242076e+00
-2.20932424e-01 2.29100093e-01 1.16722845e-01 -4.21878248... | [10.899620056152344, -3.2632813453674316] |
d3ed3b1b-c900-432a-a511-2c0cd1461fa6 | error-bounds-of-projection-models-in-weakly | 2010.12317 | null | https://arxiv.org/abs/2010.12317v1 | https://arxiv.org/pdf/2010.12317v1.pdf | Error Bounds of Projection Models in Weakly Supervised 3D Human Pose Estimation | The current state-of-the-art in monocular 3D human pose estimation is heavily influenced by weakly supervised methods. These allow 2D labels to be used to learn effective 3D human pose recovery either directly from images or via 2D-to-3D pose uplifting. In this paper we present a detailed analysis of the most commonly ... | ['Rainer Lienhart', 'Stephan Brehm', 'Moritz Einfalt', 'Nikolas Klug'] | 2020-10-23 | null | null | null | null | ['monocular-3d-human-pose-estimation', 'weakly-supervised-3d-human-pose-estimation'] | ['computer-vision', 'computer-vision'] | [ 6.09917752e-02 3.51523757e-01 -3.05976301e-01 -1.47757605e-01
-6.34970009e-01 -5.07070124e-01 6.54837012e-01 -1.52858227e-01
-7.17506289e-01 7.53441334e-01 1.94866821e-01 2.91194618e-01
-3.35787199e-02 -2.27797121e-01 -8.35578024e-01 -4.91874993e-01
-1.00680925e-01 1.07372332e+00 2.48910218e-01 -3.33298415... | [6.953007698059082, -0.9486154913902283] |
51d32580-2c21-414c-99ac-a236c6d3ff21 | a-conformer-based-asr-frontend-for-joint | 2111.09935 | null | https://arxiv.org/abs/2111.09935v1 | https://arxiv.org/pdf/2111.09935v1.pdf | A Conformer-based ASR Frontend for Joint Acoustic Echo Cancellation, Speech Enhancement and Speech Separation | We present a frontend for improving robustness of automatic speech recognition (ASR), that jointly implements three modules within a single model: acoustic echo cancellation, speech enhancement, and speech separation. This is achieved by using a contextual enhancement neural network that can optionally make use of diff... | ['Nathan Howard', 'James Walker', 'Alex Park', 'Quan Wang', 'Arun Narayanan', "Tom O'Malley"] | 2021-11-18 | null | null | null | null | ['acoustic-echo-cancellation', 'acoustic-echo-cancellation'] | ['medical', 'speech'] | [ 2.90039867e-01 -8.26415941e-02 5.28315783e-01 -7.77281970e-02
-1.30865729e+00 -3.30039680e-01 3.28307360e-01 -9.77879986e-02
-6.65414095e-01 1.03739955e-01 7.28880942e-01 -4.97873425e-01
2.72281528e-01 -1.82486370e-01 -5.66831470e-01 -7.62910545e-01
-3.05741560e-02 -1.89066425e-01 2.47356549e-01 -5.96827269... | [14.84353256225586, 6.040369033813477] |
2fa40905-7a90-4fea-87f9-888cfa7cc691 | ml-net-multi-label-classification-of | 1811.05475 | null | http://arxiv.org/abs/1811.05475v2 | http://arxiv.org/pdf/1811.05475v2.pdf | ML-Net: multi-label classification of biomedical texts with deep neural networks | In multi-label text classification, each textual document can be assigned
with one or more labels. Due to this nature, the multi-label text
classification task is often considered to be more challenging compared to the
binary or multi-class text classification problems. As an important task with
broad applications in b... | ['Jingcheng Du', 'Cui Tao', 'Qingyu Chen', 'Yifan Peng', 'Zhiyong Lu', 'Yang Xiang'] | 2018-11-13 | null | null | null | null | ['multi-label-classification-of-biomedical'] | ['medical'] | [ 4.36095268e-01 -8.54728222e-02 -3.80882591e-01 -6.01074278e-01
-1.13800824e+00 -5.13494253e-01 4.52313364e-01 8.61868739e-01
-5.59918880e-01 7.71787524e-01 -1.58581138e-01 -3.49975199e-01
-1.52866662e-01 -5.59556663e-01 -1.89307973e-01 -8.16174030e-01
3.73497337e-01 9.60840762e-01 -7.76625797e-02 2.95211375... | [9.449809074401855, 4.523286819458008] |
88efb93e-948e-4085-a289-624dd15ad3fe | hybrid-long-document-summarization-using-c2f | 2306.01169 | null | https://arxiv.org/abs/2306.01169v1 | https://arxiv.org/pdf/2306.01169v1.pdf | Hybrid Long Document Summarization using C2F-FAR and ChatGPT: A Practical Study | Text summarization is a downstream natural language processing (NLP) task that challenges the understanding and generation capabilities of language models. Considerable progress has been made in automatically summarizing short texts, such as news articles, often leading to satisfactory results. However, summarizing lon... | ['Tu Tran', 'Sylvia B. Larcher', 'Guang Lu'] | 2023-06-01 | null | null | null | null | ['text-summarization', 'extractive-summarization', 'document-summarization'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 1.58396780e-01 3.80741239e-01 -2.45855227e-01 -1.67416230e-01
-1.29156756e+00 -7.02310860e-01 9.61281836e-01 6.60463631e-01
-2.30081543e-01 8.39298010e-01 1.08339310e+00 -2.01617718e-01
-2.52411924e-02 -3.71113777e-01 -3.75154316e-01 -1.58374116e-01
1.68938920e-01 7.26339817e-01 3.07340138e-02 -4.73269016... | [12.337801933288574, 9.459553718566895] |
e93613e6-e8f8-4a4a-a157-64f7df26e158 | large-language-models-enable-few-shot | 2307.00524 | null | https://arxiv.org/abs/2307.00524v1 | https://arxiv.org/pdf/2307.00524v1.pdf | Large Language Models Enable Few-Shot Clustering | Unlike traditional unsupervised clustering, semi-supervised clustering allows users to provide meaningful structure to the data, which helps the clustering algorithm to match the user's intent. Existing approaches to semi-supervised clustering require a significant amount of feedback from an expert to improve the clust... | ['Graham Neubig', 'Tongshuang Wu', 'Carolin Lawrence', 'Kiril Gashteovski', 'Vijay Viswanathan'] | 2023-07-02 | null | null | null | null | ['clustering', 'text-clustering'] | ['methodology', 'natural-language-processing'] | [ 8.56470615e-02 9.67195034e-02 -1.44381091e-01 -8.15959394e-01
-1.15594101e+00 -9.98090208e-01 4.74745005e-01 9.17900860e-01
-4.87421155e-01 4.26338650e-02 3.90181005e-01 -5.44088960e-01
-7.27656856e-02 -4.52466965e-01 -2.35181496e-01 -4.09054816e-01
-3.70952412e-02 7.71574020e-01 2.64243275e-01 1.51031718... | [10.55150032043457, 7.004774570465088] |
1acb22e2-395c-44c4-a433-9aab0e34c427 | actigraphy-based-sleepwake-pattern-detection | 1802.07945 | null | http://arxiv.org/abs/1802.07945v1 | http://arxiv.org/pdf/1802.07945v1.pdf | Actigraphy-based Sleep/Wake Pattern Detection using Convolutional Neural Networks | Common medical conditions are often associated with sleep abnormalities.
Patients with medical disorders often suffer from poor sleep quality compared
to healthy individuals, which in turn may worsen the symptoms of the disorder.
Accurate detection of sleep/wake patterns is important in developing
personalized digital ... | ['Shai Fine', 'Nancy Yacovzada', 'Gabi Shalev', 'Yotam Frank', 'Lena Granovsky'] | 2018-02-22 | null | null | null | null | ['sleep-quality-prediction'] | ['medical'] | [-6.29569069e-02 -5.11508048e-01 -6.86372936e-01 -2.81204939e-01
-1.26348555e-01 -3.19868661e-02 1.46947354e-01 4.16475296e-01
-3.41263384e-01 7.18379736e-01 3.70637983e-01 -2.55974561e-01
-3.29241425e-01 -5.20141721e-01 1.38218477e-01 -9.01505649e-01
-2.88533658e-01 3.75364572e-01 -2.97735900e-01 8.40497029... | [13.595562934875488, 3.44077467918396] |
6d845e92-e938-4e82-af65-25f4ec1d2790 | soft-attention-improves-skin-cancer | 2105.03358 | null | https://arxiv.org/abs/2105.03358v3 | https://arxiv.org/pdf/2105.03358v3.pdf | Soft-Attention Improves Skin Cancer Classification Performance | In clinical applications, neural networks must focus on and highlight the most important parts of an input image. Soft-Attention mechanism enables a neural network toachieve this goal. This paper investigates the effectiveness of Soft-Attention in deep neural architectures. The central aim of Soft-Attention is to boost... | ['Sargur N. Srihari', 'Mingchen Gao', 'Mohammad Abuzar Shaikh', 'Soumyya Kanti Datta'] | 2021-05-05 | null | null | null | null | ['skin-cancer-classification'] | ['medical'] | [ 2.76934177e-01 4.70896900e-01 -3.21961105e-01 -2.02617154e-01
-8.05305660e-01 -1.20342433e-01 5.31164229e-01 1.02665238e-01
-7.32388079e-01 6.60358548e-01 5.18866837e-01 7.84732029e-02
-1.73767470e-03 -4.27391946e-01 -5.22592545e-01 -8.58439684e-01
5.42129166e-02 -1.21017449e-01 2.71644473e-01 -1.17272012... | [14.750205993652344, -2.6078667640686035] |
3ea6e276-3071-410b-9f64-d87b9056a693 | ordered-counterfactual-explanation-by-mixed | 2012.11782 | null | https://arxiv.org/abs/2012.11782v2 | https://arxiv.org/pdf/2012.11782v2.pdf | Ordered Counterfactual Explanation by Mixed-Integer Linear Optimization | Post-hoc explanation methods for machine learning models have been widely used to support decision-making. One of the popular methods is Counterfactual Explanation (CE), also known as Actionable Recourse, which provides a user with a perturbation vector of features that alters the prediction result. Given a perturbatio... | ['Hiroki Arimura', 'Kento Uemura', 'Yuichi Ike', 'Ken Kobayashi', 'Takuya Takagi', 'Kentaro Kanamori'] | 2020-12-22 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [ 4.33590561e-01 1.60070345e-01 -4.01443362e-01 -4.18601543e-01
-2.96422452e-01 -4.26359743e-01 4.06503677e-01 2.38047704e-01
-3.20439696e-01 1.05394912e+00 1.33869633e-01 -6.16091728e-01
-4.89317268e-01 -7.40395665e-01 -7.96528101e-01 -6.93495572e-01
6.87270463e-02 1.52228296e-01 -2.88073033e-01 -6.10752776... | [8.61865520477295, 5.538671016693115] |
78d510fd-2843-4b25-ab75-b65a2d6aeee9 | salad-part-level-latent-diffusion-for-3d | 2303.12236 | null | https://arxiv.org/abs/2303.12236v1 | https://arxiv.org/pdf/2303.12236v1.pdf | SALAD: Part-Level Latent Diffusion for 3D Shape Generation and Manipulation | We present a cascaded diffusion model based on a part-level implicit 3D representation. Our model achieves state-of-the-art generation quality and also enables part-level shape editing and manipulation without any additional training in conditional setup. Diffusion models have demonstrated impressive capabilities in da... | ['Minhyuk Sung', 'Minh Hieu Nguyen', 'Seungwoo Yoo', 'Juil Koo'] | 2023-03-21 | null | null | null | null | ['3d-shape-generation'] | ['computer-vision'] | [ 9.41651314e-02 2.08710253e-01 -6.80445433e-02 -1.02110077e-02
-4.28474069e-01 -5.61861217e-01 1.10205007e+00 -1.23925298e-01
-1.72177806e-01 3.75072449e-01 5.04210055e-01 1.82973325e-01
-6.99053109e-02 -1.00928116e+00 -6.81533635e-01 -6.22166753e-01
1.32563233e-01 8.10433209e-01 -3.30644846e-02 -4.19955164... | [9.223625183105469, -3.3666768074035645] |
9d3b646d-5d34-4b2f-af58-84a2f141e972 | adaptive-dereverberation-noise-and-interferer | 2303.07027 | null | https://arxiv.org/abs/2303.07027v1 | https://arxiv.org/pdf/2303.07027v1.pdf | Adaptive Dereverberation, Noise and Interferer Reduction Using Sparse Weighted Linearly Constrained Minimum Power Beamforming | Interfering sources, background noise and reverberation degrade speech quality and intelligibility in hearing aid applications. In this paper, we present an adaptive algorithm aiming at dereverberation, noise and interferer reduction and preservation of binaural cues based on the wBLCMP beamformer. The wBLCMP beamforme... | ['Simon Doclo', 'Henri Gode'] | 2023-03-13 | null | null | null | null | ['speech-enhancement'] | ['speech'] | [ 1.40241832e-01 -2.43083060e-01 4.10838455e-01 1.55303702e-01
-8.56210232e-01 -1.45635143e-01 3.11239436e-02 -2.14916915e-01
-5.78722596e-01 6.70003891e-01 9.82654810e-01 -3.15157115e-01
-4.89058375e-01 -2.41042227e-01 -3.73263329e-01 -1.12106740e+00
-1.04075387e-01 -4.64310527e-01 5.80293089e-02 -8.45385119... | [15.09001636505127, 5.827152729034424] |
72a417a5-4f64-41e5-83b3-99b3ca31a38f | difference-in-differences-with-time-varying | 2202.02903 | null | https://arxiv.org/abs/2202.02903v2 | https://arxiv.org/pdf/2202.02903v2.pdf | Difference-in-Differences with Time-Varying Covariates in the Parallel Trends Assumption | In this paper, we study difference-in-differences identification and estimation strategies where the parallel trends assumption holds after conditioning on time-varying covariates and/or time-invariant covariates. Our first main contribution is to point out a number of weaknesses of commonly used two-way fixed effects ... | ['Brantly Callaway', 'Carolina Caetano'] | 2022-02-07 | null | null | null | null | ['econometrics'] | ['miscellaneous'] | [ 2.20193371e-01 -2.10197866e-01 -9.41444814e-01 -1.52559072e-01
-6.99944675e-01 -5.47317207e-01 7.17251897e-01 3.87903959e-01
-6.78863287e-01 9.98179972e-01 6.12416387e-01 -8.35469842e-01
-8.80907714e-01 -6.47900641e-01 -5.62689543e-01 -5.26912987e-01
-2.66011655e-01 5.38627096e-02 -8.51740986e-02 2.30446398... | [7.916110038757324, 5.205323219299316] |
32068467-aa13-464b-a31e-c6ee1a98edfb | automatic-instrument-recognition-in | 1511.05520 | null | http://arxiv.org/abs/1511.05520v1 | http://arxiv.org/pdf/1511.05520v1.pdf | Automatic Instrument Recognition in Polyphonic Music Using Convolutional Neural Networks | Traditional methods to tackle many music information retrieval tasks
typically follow a two-step architecture: feature engineering followed by a
simple learning algorithm. In these "shallow" architectures, feature
engineering and learning are typically disjoint and unrelated. Additionally,
feature engineering is diffic... | ['Peter Li', 'Tian Wang', 'Jiyuan Qian'] | 2015-11-17 | null | null | null | null | ['instrument-recognition'] | ['audio'] | [ 2.95146137e-01 -3.77058536e-01 2.39452615e-01 -3.85203123e-01
-9.94199455e-01 -9.59192753e-01 4.18051392e-01 1.37520730e-01
-5.07614493e-01 2.29955927e-01 -9.27632600e-02 -1.37889087e-01
-4.49831277e-01 -4.52745229e-01 -6.34365797e-01 -1.42058626e-01
-1.73821002e-01 3.16006035e-01 -2.03337669e-01 -3.59993666... | [15.782386779785156, 5.238184452056885] |
c5be3985-bea3-4866-93a6-085833f6334b | ultrasonic-image-s-annotation-removal-a-self | 2307.04133 | null | https://arxiv.org/abs/2307.04133v1 | https://arxiv.org/pdf/2307.04133v1.pdf | Ultrasonic Image's Annotation Removal: A Self-supervised Noise2Noise Approach | Accurately annotated ultrasonic images are vital components of a high-quality medical report. Hospitals often have strict guidelines on the types of annotations that should appear on imaging results. However, manually inspecting these images can be a cumbersome task. While a neural network could potentially automate th... | ['Yueyang Teng', 'Junying Cao', 'Zhaoheng Xie', 'Nan Jiang', 'Yuanheng Zhang'] | 2023-07-09 | null | null | null | null | ['denoising'] | ['computer-vision'] | [ 4.88266438e-01 3.81552398e-01 2.65810311e-01 -6.27527237e-01
-1.44721639e+00 -5.75474679e-01 1.13613367e-01 2.60223389e-01
-6.19747937e-01 4.32265729e-01 4.30729706e-03 -2.54523396e-01
7.23106414e-02 -4.54045832e-01 -7.91122794e-01 -8.65404427e-01
8.76755267e-02 4.08608437e-01 3.73437941e-01 9.28699970... | [14.50753402709961, -2.307673454284668] |
2bfabb1e-6f3e-4ced-8260-9d3645a4bfa1 | autooptlib-a-library-of-automatically | 2303.06536 | null | https://arxiv.org/abs/2303.06536v1 | https://arxiv.org/pdf/2303.06536v1.pdf | AutoOptLib: A Library of Automatically Designing Metaheuristic Optimization Algorithms in MATLAB | Metaheuristic algorithms are widely-recognized solvers for challenging optimization problems with multi-modality, discretization, large-scale, multi-objectivity, etc. Automatically designing metaheuristic algorithms leverages today's increasing computing resources to conceive, build up, and verify the design choices of... | ['Yuhui Shi', 'Xianglong Chen', 'Taiwei Hu', 'Bai Yan', 'Qi Zhao'] | 2023-03-12 | null | null | null | null | ['metaheuristic-optimization'] | ['methodology'] | [-1.46162659e-01 -5.06296992e-01 -4.18128707e-02 7.20569193e-02
-4.46762174e-01 -9.47640240e-01 1.33502424e-01 -2.13655129e-01
1.03962332e-01 1.09899259e+00 -3.20036262e-01 -4.71135288e-01
-7.61003911e-01 -8.05482626e-01 -3.51978749e-01 -8.91450763e-01
-1.89778179e-01 6.03446841e-01 -1.54730305e-01 -3.15022707... | [5.736851692199707, 3.6100149154663086] |
a311f197-97cc-42e6-bbc8-89a6fe33d36b | plug-and-play-recipe-generation-with-content | 2212.05093 | null | https://arxiv.org/abs/2212.05093v1 | https://arxiv.org/pdf/2212.05093v1.pdf | Plug-and-Play Recipe Generation with Content Planning | Recent pre-trained language models have shown promising capabilities in generating fluent and realistic natural language text. However, generating multi-sentence text with global content planning has been a long-existing research question. Current approaches for controlled text generation can hardly address this issue,... | ['Nigel Collier', 'Ehsan Shareghi', 'Yixuan Su', 'Yinhong Liu'] | 2022-12-09 | null | null | null | null | ['recipe-generation'] | ['miscellaneous'] | [ 1.29078180e-01 1.55284837e-01 -8.17672387e-02 -2.46619821e-01
-1.00694251e+00 -5.70491135e-01 1.09814215e+00 2.24162161e-01
-2.38285527e-01 8.85188699e-01 6.11790419e-01 -1.78659678e-01
4.72487301e-01 -1.01951742e+00 -8.98871899e-01 -3.82782310e-01
2.40420520e-01 7.75797427e-01 4.81360778e-03 -4.77901518... | [11.752083778381348, 8.957332611083984] |
0ac42fcc-7375-4e49-9b41-807ce83f5aa5 | refsr-nerf-towards-high-fidelity-and-super | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Huang_RefSR-NeRF_Towards_High_Fidelity_and_Super_Resolution_View_Synthesis_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Huang_RefSR-NeRF_Towards_High_Fidelity_and_Super_Resolution_View_Synthesis_CVPR_2023_paper.pdf | RefSR-NeRF: Towards High Fidelity and Super Resolution View Synthesis | We present Reference-guided Super-Resolution Neural Radiance Field (RefSR-NeRF) that extends NeRF to super resolution and photorealistic novel view synthesis. Despite NeRF's extraordinary success in the neural rendering field, it suffers from blur in high resolution rendering because its inherent multilayer percept... | ['Yunhe Wang', 'Hanting Chen', 'Jie Hu', 'Wei Li', 'Xudong Huang'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['neural-rendering', 'novel-view-synthesis'] | ['computer-vision', 'computer-vision'] | [ 5.40489733e-01 -5.32397479e-02 2.12657154e-01 -3.16872180e-01
-9.44996655e-01 -1.41138941e-01 6.84202313e-01 -6.32557392e-01
1.04633346e-02 7.16373861e-01 5.88189960e-01 -1.08359708e-02
-2.38869917e-02 -7.80532598e-01 -9.60207582e-01 -6.22324109e-01
2.03821346e-01 -4.08341140e-02 7.79998153e-02 -5.76519072... | [10.939817428588867, -2.083038568496704] |
f55502dc-1b4a-45a8-b056-985b84ed6188 | post-hoc-selection-of-pareto-optimal | 2306.12165 | null | https://arxiv.org/abs/2306.12165v1 | https://arxiv.org/pdf/2306.12165v1.pdf | Post-hoc Selection of Pareto-Optimal Solutions in Search and Recommendation | Information Retrieval (IR) and Recommender Systems (RS) tasks are moving from computing a ranking of final results based on a single metric to multi-objective problems. Solving these problems leads to a set of Pareto-optimal solutions, known as Pareto frontier, in which no objective can be further improved without hurt... | ['Tommaso Di Noia', 'Raffaele Perego', 'Franco Maria Nardini', 'Vito Walter Anelli', 'Vincenzo Paparella'] | 2023-06-21 | null | null | null | null | ['information-retrieval'] | ['natural-language-processing'] | [ 6.01233691e-02 -2.05076292e-01 -2.12186173e-01 -2.10007615e-02
-7.76071191e-01 -6.98263109e-01 2.49113441e-01 3.64159763e-01
-3.87764812e-01 5.76298773e-01 1.52955875e-01 -9.75613073e-02
-1.21730554e+00 -7.40787089e-01 -4.47031796e-01 -8.62283170e-01
7.05416203e-02 7.62044549e-01 -5.31183509e-03 -3.65076751... | [9.609007835388184, 5.586344242095947] |
b6749c34-c596-41ec-a8e8-4c97d6d7c39f | aenet-learning-deep-audio-features-for-video | 1701.00599 | null | http://arxiv.org/abs/1701.00599v2 | http://arxiv.org/pdf/1701.00599v2.pdf | AENet: Learning Deep Audio Features for Video Analysis | We propose a new deep network for audio event recognition, called AENet. In
contrast to speech, sounds coming from audio events may be produced by a wide
variety of sources. Furthermore, distinguishing them often requires analyzing
an extended time period due to the lack of clear sub-word units that are
present in spee... | ['Luc van Gool', 'Naoya Takahashi', 'Michael Gygli'] | 2017-01-03 | null | null | null | null | ['highlight-detection'] | ['computer-vision'] | [ 2.49994427e-01 -3.15617383e-01 2.83330292e-01 -2.58020945e-02
-8.78501594e-01 -5.00674129e-01 4.12266165e-01 4.42066103e-01
-6.10517204e-01 3.90076071e-01 3.93029809e-01 1.12028509e-01
2.29747251e-01 -6.49930060e-01 -8.50321174e-01 -4.46396887e-01
-3.55817080e-01 -3.03322762e-01 5.05217373e-01 1.03896305... | [15.192208290100098, 5.234137535095215] |
e90b1c75-3cd4-480f-96e3-c2e999b677ab | making-a-case-for-3d-convolutions-for-object | 2008.11516 | null | https://arxiv.org/abs/2008.11516v1 | https://arxiv.org/pdf/2008.11516v1.pdf | Making a Case for 3D Convolutions for Object Segmentation in Videos | The task of object segmentation in videos is usually accomplished by processing appearance and motion information separately using standard 2D convolutional networks, followed by a learned fusion of the two sources of information. On the other hand, 3D convolutional networks have been successfully applied for video cla... | ['Laura Leal-Taixé', 'Aljoša Ošep', 'Sabarinath Mahadevan', 'Bastian Leibe', 'Sebastian Hennen', 'Ali Athar'] | 2020-08-26 | null | null | null | null | ['unsupervised-video-object-segmentation'] | ['computer-vision'] | [ 4.33662266e-01 2.62395173e-01 -2.87452370e-01 -3.26984465e-01
-6.07237816e-01 -3.85978997e-01 6.17311358e-01 -1.65232748e-01
-5.28429210e-01 2.67021745e-01 9.42086503e-02 -3.01425457e-01
3.12535346e-01 -4.44420308e-01 -1.16589618e+00 -5.04306257e-01
-3.09998274e-01 3.82451624e-01 5.94996870e-01 8.64454582... | [9.220084190368652, 0.03214976191520691] |
09844273-82d2-4d37-97d9-a141d6411ccb | tackling-universal-properties-of-minimal-trap | 2305.02442 | null | https://arxiv.org/abs/2305.02442v1 | https://arxiv.org/pdf/2305.02442v1.pdf | Tackling Universal Properties of Minimal Trap Spaces of Boolean Networks | Minimal trap spaces (MTSs) capture subspaces in which the Boolean dynamics is trapped, whatever the update mode. They correspond to the attractors of the most permissive mode. Due to their versatility, the computation of MTSs has recently gained traction, essentially by focusing on their enumeration. In this paper, we ... | ['Loïc Paulevé', 'Gustavo Magaña López', 'Jean-Marie Lagniez', 'Sara Riva'] | 2023-05-03 | null | null | null | null | ['logical-reasoning'] | ['reasoning'] | [ 5.01540482e-01 5.86865187e-01 -2.21540079e-01 -6.14490546e-03
-1.87031716e-01 -8.84951055e-01 3.36274892e-01 2.19699502e-01
-1.15081044e-02 1.25463259e+00 -2.77877599e-01 -7.38565087e-01
-8.60761583e-01 -1.02715099e+00 -7.44758070e-01 -8.19054902e-01
-5.56766152e-01 6.75935805e-01 5.61953187e-01 -5.23433506... | [8.647235870361328, 6.792140960693359] |
6998eed3-8631-4565-92a2-250caa3fd6ff | novice-type-error-diagnosis-with-natural | 2210.03682 | null | https://arxiv.org/abs/2210.03682v1 | https://arxiv.org/pdf/2210.03682v1.pdf | Novice Type Error Diagnosis with Natural Language Models | Strong static type systems help programmers eliminate many errors without much burden of supplying type annotations. However, this flexibility makes it highly non-trivial to diagnose ill-typed programs, especially for novice programmers. Compared to classic constraint solving and optimization-based approaches, the data... | ['Xujie Si', 'Brigitte Pientka', 'Tianyu Han', 'Yixuan Li', 'Haolin Ye', 'Chuqin Geng'] | 2022-10-07 | null | null | null | null | ['type'] | ['speech'] | [-3.77641618e-02 1.37651339e-01 -2.71301746e-01 -4.75783199e-01
-8.34835947e-01 -5.44324279e-01 1.01986423e-01 6.75195098e-01
-3.54416937e-01 3.41819048e-01 -2.60536522e-01 -8.28335226e-01
-4.78376774e-03 -7.55809367e-01 -8.10598850e-01 1.78120166e-01
-5.33076860e-02 2.86772043e-01 2.99028784e-01 -2.22386241... | [7.762411117553711, 7.725215911865234] |
faaeeeae-2983-4386-85ef-5d4b4524c70a | sound-to-visual-scene-generation-by-audio-to | 2303.17490 | null | https://arxiv.org/abs/2303.17490v1 | https://arxiv.org/pdf/2303.17490v1.pdf | Sound to Visual Scene Generation by Audio-to-Visual Latent Alignment | How does audio describe the world around us? In this paper, we propose a method for generating an image of a scene from sound. Our method addresses the challenges of dealing with the large gaps that often exist between sight and sound. We design a model that works by scheduling the learning procedure of each model comp... | ['Tae-Hyun Oh', 'Andrew Owens', 'Hyunwoo Ha', 'Arda Senocak', 'Kim Sung-Bin'] | 2023-03-30 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Sung-Bin_Sound_to_Visual_Scene_Generation_by_Audio-to-Visual_Latent_Alignment_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Sung-Bin_Sound_to_Visual_Scene_Generation_by_Audio-to-Visual_Latent_Alignment_CVPR_2023_paper.pdf | cvpr-2023-1 | ['scene-generation'] | ['computer-vision'] | [ 4.13476706e-01 -8.17531124e-02 2.89373338e-01 -2.77594239e-01
-1.22995222e+00 -9.26433086e-01 6.68817043e-01 -2.61647940e-01
1.48143545e-01 2.40988150e-01 5.25993288e-01 -4.85904552e-02
1.83859348e-01 -6.45516574e-01 -7.55391121e-01 -5.55995047e-01
-1.42271295e-01 1.00551181e-01 1.80492923e-01 1.24174170... | [15.203411102294922, 5.129062652587891] |
5b1734ce-920f-4a6e-ac9a-a0fccfe9c656 | bsrt-improving-burst-super-resolution-with | 2204.08332 | null | https://arxiv.org/abs/2204.08332v2 | https://arxiv.org/pdf/2204.08332v2.pdf | BSRT: Improving Burst Super-Resolution with Swin Transformer and Flow-Guided Deformable Alignment | This work addresses the Burst Super-Resolution (BurstSR) task using a new architecture, which requires restoring a high-quality image from a sequence of noisy, misaligned, and low-resolution RAW bursts. To overcome the challenges in BurstSR, we propose a Burst Super-Resolution Transformer (BSRT), which can significantl... | ['Shuaicheng Liu', 'Jian Sun', 'Haoqiang Fan', 'Zhihong Wen', 'Qi Wu', 'Lei Yu', 'Shen Cheng', 'Youwei Li', 'Ziwei Luo'] | 2022-04-18 | null | null | null | null | ['multi-frame-super-resolution', 'burst-image-super-resolution'] | ['computer-vision', 'computer-vision'] | [ 1.21700831e-01 -6.03643894e-01 4.73757610e-02 -2.96744436e-01
-7.28202760e-01 -3.13618660e-01 4.23488528e-01 -7.33827710e-01
-1.03921510e-01 8.77837598e-01 6.92923307e-01 2.77430952e-01
-6.08869568e-02 -7.91714847e-01 -8.55210960e-01 -4.55031395e-01
-6.84769675e-02 -1.44087419e-01 7.43360519e-01 -5.02182186... | [11.02558708190918, -1.9052196741104126] |
40fa00fc-e0da-4120-802e-f7f030a7b4bc | negation-detection-in-dutch-spoken-human | null | null | https://aclanthology.org/2022.lrec-1.56 | https://aclanthology.org/2022.lrec-1.56.pdf | Negation Detection in Dutch Spoken Human-Computer Conversations | Proper recognition and interpretation of negation signals in text or communication is crucial for any form of full natural language understanding. It is also essential for computational approaches to natural language processing. In this study we focus on negation detection in Dutch spoken human-computer conversations. ... | ['Helmer Strik', 'Iris Hendrickx', 'Tom Sweers'] | null | null | null | null | lrec-2022-6 | ['negation-detection'] | ['natural-language-processing'] | [ 2.15974897e-01 2.67326146e-01 8.71450230e-02 -7.53167927e-01
-9.32508945e-01 -8.49597633e-01 7.48831213e-01 3.43316197e-01
-1.06537783e+00 1.12092447e+00 4.66863751e-01 -5.00041187e-01
4.68454808e-01 -6.71840072e-01 -5.13035059e-01 -4.52311113e-02
1.29443750e-01 6.37996078e-01 4.51114208e-01 -9.30670559... | [10.5242919921875, 9.297021865844727] |
04e58659-7568-45ba-a22c-6d5cfa2df59f | artificial-life-using-the-book-and-bookmarker | 2210.12854 | null | https://arxiv.org/abs/2210.12854v2 | https://arxiv.org/pdf/2210.12854v2.pdf | Artificial Life using a Book and Bookmarker | Reproduction, development, and individual interactions are essential topics in artificial life. The cellular automata, which can handle these in a composite way, is highly restricted in its form and behavior because it represents life as a pattern of cells. In contrast, the virtual creatures proposed by Karl Sims have ... | ['Keishu Utimula'] | 2022-10-07 | null | null | null | null | ['artificial-life'] | ['miscellaneous'] | [-2.59873480e-01 -4.88644466e-02 2.68830597e-01 4.07587916e-01
1.08682013e+00 -7.51079857e-01 1.04821873e+00 1.05753615e-01
-2.19124362e-01 9.99553144e-01 -3.16300392e-01 -7.57953525e-02
-1.38826936e-01 -1.30362117e+00 -2.61369765e-01 -9.45265770e-01
-1.97111726e-01 5.99991024e-01 3.33886951e-01 -5.77315629... | [5.6089348793029785, 4.150115966796875] |
d60947d1-4e78-4796-a96f-f2ef80001667 | formalisation-of-action-with-durations-in | 2109.08305 | null | https://arxiv.org/abs/2109.08305v1 | https://arxiv.org/pdf/2109.08305v1.pdf | Formalisation of Action with Durations in Answer Set Programming | In this paper, I will discuss the work I am currently doing as a Ph.D. student at the University of Potsdam, under the tutoring of T. Schaub. I'm currently looking into action description in ASP. More precisely, my goal is to explore how to represent actions with durations in ASP, in different contexts. Right now, I'm ... | ['Etienne Tignon'] | 2021-09-17 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [-2.85743922e-02 3.08464259e-01 -1.18862972e-01 -2.28666827e-01
-1.96777154e-02 -7.76222944e-01 3.78626466e-01 4.44519877e-01
-3.51102591e-01 9.33816850e-01 2.85088301e-01 -5.65670848e-01
-5.67486525e-01 -1.03666329e+00 -1.93314865e-01 -2.12844372e-01
-7.60825351e-02 6.00767672e-01 5.98303795e-01 -4.90038186... | [3.576200008392334, 1.3976904153823853] |
0310e954-166c-4b2a-9ab3-dddfd652c8b9 | diffusion-models-a-comprehensive-survey-of | 2209.00796 | null | https://arxiv.org/abs/2209.00796v9 | https://arxiv.org/pdf/2209.00796v9.pdf | Diffusion Models: A Comprehensive Survey of Methods and Applications | Diffusion models have emerged as a powerful new family of deep generative models with record-breaking performance in many applications, including image synthesis, video generation, and molecule design. In this survey, we provide an overview of the rapidly expanding body of work on diffusion models, categorizing the res... | ['Ming-Hsuan Yang', 'Yingxia Shao', 'Yue Zhao', 'Runsheng Xu', 'Shenda Hong', 'Yang song', 'Bin Cui', 'Wentao Zhang', 'Zhilong Zhang', 'Ling Yang'] | 2022-09-02 | null | null | null | null | ['video-generation'] | ['computer-vision'] | [ 1.34983808e-02 -2.26787940e-01 -5.70573151e-01 -4.72005159e-02
-4.59154129e-01 -6.17172301e-01 1.02286863e+00 -3.07768553e-01
-1.55367032e-01 6.37796223e-01 4.12289709e-01 -3.63609701e-01
-2.23627195e-01 -8.24209511e-01 -3.81534189e-01 -1.03337228e+00
-2.73703068e-01 5.00850320e-01 -3.38922963e-02 1.96686760... | [11.272176742553711, 0.00010863344505196437] |
8d02e6fd-834f-416d-b18d-3def3ccf8e90 | pttr-relational-3d-point-cloud-object | 2112.02857 | null | https://arxiv.org/abs/2112.02857v5 | https://arxiv.org/pdf/2112.02857v5.pdf | PTTR: Relational 3D Point Cloud Object Tracking with Transformer | In a point cloud sequence, 3D object tracking aims to predict the location and orientation of an object in the current search point cloud given a template point cloud. Motivated by the success of transformers, we propose Point Tracking TRansformer (PTTR), which efficiently predicts high-quality 3D tracking results in a... | ['Shijian Lu', 'Haiyu Zhao', 'Zhongang Cai', 'Liang Pan', 'Tianrui Liu', 'Yueru Luo', 'Zhipeng Luo', 'Changqing Zhou'] | 2021-12-06 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Zhou_PTTR_Relational_3D_Point_Cloud_Object_Tracking_With_Transformer_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Zhou_PTTR_Relational_3D_Point_Cloud_Object_Tracking_With_Transformer_CVPR_2022_paper.pdf | cvpr-2022-1 | ['3d-object-tracking'] | ['computer-vision'] | [-5.84557056e-02 -3.01318616e-01 -8.21006894e-02 -1.93645835e-01
-7.60622263e-01 -2.54876971e-01 5.71032107e-01 7.57252276e-02
-6.74601719e-02 9.90965217e-02 5.73450215e-02 -3.13213356e-02
-1.54069677e-01 -8.84686410e-01 -1.13396299e+00 -4.90980625e-01
2.21293136e-01 6.06148243e-01 7.69783854e-01 -6.49745762... | [6.641216278076172, -2.388139009475708] |
16144c61-c89c-492a-ba15-8ec78ad6431a | enhanced-characterness-for-text-detection-in | 1712.04927 | null | http://arxiv.org/abs/1712.04927v1 | http://arxiv.org/pdf/1712.04927v1.pdf | Enhanced Characterness for Text Detection in the Wild | Text spotting is an interesting research problem as text may appear at any
random place and may occur in various forms. Moreover, ability to detect text
opens the horizons for improving many advanced computer vision problems. In
this paper, we propose a novel language agnostic text detection method
utilizing edge enhan... | ['Brejesh lall', 'Siddharth Srivastava', 'Prerana Mukherjee', 'Aarushi Agrawal'] | 2017-12-04 | null | null | null | null | ['text-spotting'] | ['computer-vision'] | [ 6.37937784e-01 -4.56158280e-01 -2.31987461e-01 -1.06108077e-01
-5.27002692e-01 -6.52546704e-01 8.14983726e-01 3.52631122e-01
-5.00922203e-01 7.74419785e-01 6.30347356e-02 -1.89858481e-01
4.14978974e-02 -5.83036840e-01 -8.09964910e-02 -7.74892449e-01
2.76037186e-01 4.20104831e-01 6.39097035e-01 -1.62002668... | [11.987567901611328, 2.3229868412017822] |
f8c37545-a718-4a07-9709-4f29afc1946b | selective-frequency-network-for-image | null | null | https://openreview.net/forum?id=tyZ1ChGZIKO | https://openreview.net/forum?id=tyZ1ChGZIKO | Selective Frequency Network for Image Restoration | Image restoration aims to reconstruct the latent sharp image from its corrupted counterpart. Besides dealing with this long-standing task in the spatial domain, a few approaches seek solutions in the frequency domain in consideration of the large discrepancy between spectra of sharp/degraded image pairs. However, these... | ['Alois Knoll', 'Kai Huang', 'Xiaochun Cao', 'Xinwei Gao', 'Wenqi Ren', 'Zhenshan Bing', 'Yi Tao', 'Yuning Cui'] | 2023-04-13 | null | null | null | conference-2023-4 | ['image-dehazing', 'deblurring'] | ['computer-vision', 'computer-vision'] | [ 6.36953473e-01 -3.91758710e-01 -3.00559644e-02 -2.04203874e-01
-7.44953454e-01 -4.27836150e-01 3.46854866e-01 -2.85387456e-01
-3.43487382e-01 8.07131052e-01 6.19935274e-01 1.63916081e-01
-4.45810944e-01 -6.64242327e-01 -7.32999384e-01 -1.23956370e+00
1.29001737e-01 -5.95116854e-01 2.51898885e-01 -2.05951110... | [11.312270164489746, -2.336535930633545] |
69ff2f73-4218-4621-b053-aab7c02ba2d4 | dave-a-unified-framework-for-fast-vehicle | 1607.04564 | null | http://arxiv.org/abs/1607.04564v3 | http://arxiv.org/pdf/1607.04564v3.pdf | DAVE: A Unified Framework for Fast Vehicle Detection and Annotation | Vehicle detection and annotation for streaming video data with complex scenes
is an interesting but challenging task for urban traffic surveillance. In this
paper, we present a fast framework of Detection and Annotation for Vehicles
(DAVE), which effectively combines vehicle detection and attributes annotation.
DAVE co... | ['Matt Mellor', 'Li Liu', 'Yi Zhou', 'Ling Shao'] | 2016-07-15 | null | null | null | null | ['fast-vehicle-detection'] | ['computer-vision'] | [ 6.61923885e-02 -8.23396668e-02 -2.00886056e-01 -6.89117134e-01
-8.25339317e-01 -4.78785306e-01 7.51429319e-01 -1.86844930e-01
-5.21039546e-01 3.95096540e-01 -2.48375684e-01 -3.54691923e-01
6.40541732e-01 -6.64861262e-01 -1.04476547e+00 -7.52731025e-01
-2.09070399e-01 5.55500805e-01 9.40566957e-01 8.40748623... | [8.089118957519531, -1.4612313508987427] |
0bd88a5e-8b7e-49ad-bb02-9cff24af2037 | dual-semantic-knowledge-composed-multimodal | 2305.09990 | null | https://arxiv.org/abs/2305.09990v1 | https://arxiv.org/pdf/2305.09990v1.pdf | Dual Semantic Knowledge Composed Multimodal Dialog Systems | Textual response generation is an essential task for multimodal task-oriented dialog systems.Although existing studies have achieved fruitful progress, they still suffer from two critical limitations: 1) focusing on the attribute knowledge but ignoring the relation knowledge that can reveal the correlations between dif... | ['Tat-Seng Chua', 'Liqiang Nie', 'Yinwei Wei', 'Xuemeng Song', 'Xiaolin Chen'] | 2023-05-17 | null | null | null | null | ['response-generation'] | ['natural-language-processing'] | [ 2.35150024e-01 2.69528240e-01 -1.94970414e-01 -4.91583228e-01
-7.69055367e-01 -1.86245084e-01 5.22125006e-01 -1.04802661e-02
-3.73144746e-01 7.51715302e-01 5.58020353e-01 -8.97831097e-02
-3.25113297e-01 -8.46257865e-01 -1.74548715e-01 -6.18029356e-01
5.14332831e-01 6.03914618e-01 2.33653247e-01 -5.92864215... | [12.34627914428711, 7.934515476226807] |
6e174b2b-8778-43d6-b471-2c25a8ff7ee6 | viewrefer-grasp-the-multi-view-knowledge-for | 2303.16894 | null | https://arxiv.org/abs/2303.16894v2 | https://arxiv.org/pdf/2303.16894v2.pdf | ViewRefer: Grasp the Multi-view Knowledge for 3D Visual Grounding with GPT and Prototype Guidance | Understanding 3D scenes from multi-view inputs has been proven to alleviate the view discrepancy issue in 3D visual grounding. However, existing methods normally neglect the view cues embedded in the text modality and fail to weigh the relative importance of different views. In this paper, we propose ViewRefer, a multi... | ['Xuelong Li', 'Bin Zhao', 'Zhigang Wang', 'Dong Wang', 'Renrui Zhang', 'Yiwen Tang', 'Ziyu Guo'] | 2023-03-29 | null | null | null | null | ['visual-grounding'] | ['computer-vision'] | [-1.84670329e-01 -1.38344929e-01 -1.63161635e-01 -5.90413153e-01
-7.54862666e-01 -7.21229613e-01 7.28358448e-01 -1.58179030e-01
1.88415170e-01 -3.39128971e-02 6.20569050e-01 -1.59477547e-01
1.34687796e-01 -6.51505232e-01 -7.61756003e-01 -3.67794722e-01
6.08309269e-01 3.99163365e-01 1.50626808e-01 -2.73990303... | [8.219754219055176, -3.4956963062286377] |
f7676539-6846-458b-8f44-6be0e53ed535 | using-deep-convolutional-neural-networks-for | null | null | https://www.frontiersin.org/articles/10.3389/fnins.2020.00207/full?report=reader | https://www.frontiersin.org/articles/10.3389/fnins.2020.00207/full?report=reader | Using deep convolutional neural networks for neonatal brain image segmentation | Introduction: Deep learning neural networks are especially potent at dealing with structured data, such as images and volumes. Both modified LiviaNET and HyperDense-Net performed well at a prior competition segmenting 6-month-old infant magnetic resonance images, but neonatal cerebral tissue type identification is chal... | ['Lodygensky GA.', 'Dolz J', 'Luck D', 'Ortmann J', 'Suffren S', 'Enguix V', 'Acosta R', 'Ding Y'] | 2020-03-26 | null | null | null | null | ['brain-image-segmentation'] | ['medical'] | [ 2.05123857e-01 4.18465108e-01 -2.35281400e-02 -6.29564166e-01
-8.24316204e-01 -6.65439606e-01 1.40051305e-01 2.66828209e-01
-8.46793473e-01 6.21121585e-01 -8.80055279e-02 -4.26962197e-01
-1.21541992e-01 -3.23825866e-01 -7.39320934e-01 -5.66189289e-01
-5.94686031e-01 9.06772733e-01 2.67149508e-01 3.57116133... | [14.159113883972168, -2.3087477684020996] |
141c2ac6-78f7-4f88-96ce-fed4723ca0b5 | revisiting-the-stack-based-inverse-tone | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhang_Revisiting_the_Stack-Based_Inverse_Tone_Mapping_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhang_Revisiting_the_Stack-Based_Inverse_Tone_Mapping_CVPR_2023_paper.pdf | Revisiting the Stack-Based Inverse Tone Mapping | Current stack-based inverse tone mapping (ITM) methods can recover high dynamic range (HDR) radiance by predicting a set of multi-exposure images from a single low dynamic range image. However, there are still some limitations. On the one hand, these methods estimate a fixed number of images (e.g., three exposure-u... | ['Ronggang Wang', 'Yang Zhao', 'Yuyao Ye', 'Ning Zhang'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['tone-mapping', 'inverse-tone-mapping'] | ['computer-vision', 'computer-vision'] | [ 6.52369559e-01 -5.43597043e-01 3.02277267e-01 -6.27085626e-01
-7.45155573e-01 -1.31811798e-01 3.75056952e-01 -5.31633735e-01
-2.44941249e-01 3.32459539e-01 -3.76994610e-02 -1.51558012e-01
-2.16731325e-01 -9.92710233e-01 -7.12922096e-01 -6.66335344e-01
4.81930614e-01 -1.64109379e-01 5.61554193e-01 -3.77990603... | [10.92519760131836, -2.15541672706604] |
8cc1478e-f7cd-4d52-bbd5-28d4f1921317 | higher-order-generalization-bounds-learning | 2203.15972 | null | https://arxiv.org/abs/2203.15972v1 | https://arxiv.org/pdf/2203.15972v1.pdf | Higher-Order Generalization Bounds: Learning Deep Probabilistic Programs via PAC-Bayes Objectives | Deep Probabilistic Programming (DPP) allows powerful models based on recursive computation to be learned using efficient deep-learning optimization techniques. Additionally, DPP offers a unified perspective, where inference and learning algorithms are treated on a par with models as stochastic programs. Here, we offer ... | ['Mark Gerstein', 'Jonathan Warrell'] | 2022-03-30 | null | null | null | null | ['probabilistic-programming'] | ['methodology'] | [ 1.41491309e-01 3.46956044e-01 -5.95752180e-01 -4.25357372e-01
-1.49593222e+00 -7.81575799e-01 6.67878747e-01 -4.91224276e-03
-7.71030113e-02 8.46368670e-01 1.18424684e-01 -1.15769327e-01
-5.16816735e-01 -7.39945769e-01 -1.16673529e+00 -9.99677420e-01
-2.43283972e-01 7.88014352e-01 1.65325031e-01 1.15498729... | [7.013063907623291, 4.068179607391357] |
96952dc0-21d8-427f-aefd-aa6697fa7f15 | what-makes-a-question-inquisitive-a-study-on | 2205.08056 | null | https://arxiv.org/abs/2205.08056v3 | https://arxiv.org/pdf/2205.08056v3.pdf | "What makes a question inquisitive?" A Study on Type-Controlled Inquisitive Question Generation | We propose a type-controlled framework for inquisitive question generation. We annotate an inquisitive question dataset with question types, train question type classifiers, and finetune models for type-controlled question generation. Empirical results demonstrate that we can generate a variety of questions that adhere... | ['Kevin Gimpel', 'Debanjan Ghosh', 'Lingyu Gao'] | 2022-05-17 | null | null | null | null | ['question-selection'] | ['natural-language-processing'] | [ 1.92220315e-01 6.54462934e-01 2.30762973e-01 -5.72868824e-01
-1.60988569e+00 -1.08201790e+00 7.36175239e-01 2.75471300e-01
-4.42940533e-01 8.03687990e-01 5.25562108e-01 -5.37071407e-01
-2.70294070e-01 -5.98810315e-01 -3.07727784e-01 6.64425781e-03
5.06667376e-01 8.00117552e-01 5.83710670e-01 -5.68263948... | [11.595683097839355, 8.159745216369629] |
a9d9cdaf-005f-42b2-9331-f03013f1cf91 | coseg-cognitively-inspired-unsupervised | 2109.15170 | null | https://arxiv.org/abs/2109.15170v1 | https://arxiv.org/pdf/2109.15170v1.pdf | CoSeg: Cognitively Inspired Unsupervised Generic Event Segmentation | Some cognitive research has discovered that humans accomplish event segmentation as a side effect of event anticipation. Inspired by this discovery, we propose a simple yet effective end-to-end self-supervised learning framework for event segmentation/boundary detection. Unlike the mainstream clustering-based methods, ... | ['Jiebo Luo', 'Tao Mei', 'Jingen Liu', 'Xiao Wang'] | 2021-09-30 | null | null | null | null | ['boundary-detection'] | ['computer-vision'] | [ 2.19065100e-01 -4.77172099e-02 -1.01404749e-01 -5.56798697e-01
-7.21514642e-01 -3.80444795e-01 7.04185605e-01 5.26250482e-01
-6.31034732e-01 4.01871175e-01 6.62188947e-01 3.59375834e-01
1.13102384e-01 -8.28833640e-01 -6.51651323e-01 -4.07818794e-01
-1.07792698e-01 -7.88558275e-02 4.51227158e-01 -1.37844011... | [8.550943374633789, 0.5661830902099609] |
c2ca5fef-a816-4df8-841a-9f35d2a87f7a | a-semantic-network-based-evolutionary | 1404.7765 | null | http://arxiv.org/abs/1404.7765v2 | http://arxiv.org/pdf/1404.7765v2.pdf | A semantic network-based evolutionary algorithm for computational creativity | We introduce a novel evolutionary algorithm (EA) with a semantic
network-based representation. For enabling this, we establish new formulations
of EA variation operators, crossover and mutation, that we adapt to work on
semantic networks. The algorithm employs commonsense reasoning to ensure all
operations preserve the... | ['Atilim Gunes Baydin', 'Santiago Ontanon', 'Ramon Lopez de Mantaras'] | 2014-04-30 | null | null | null | null | ['analogical-similarity'] | ['reasoning'] | [ 5.03687799e-01 5.26384473e-01 1.27585232e-01 9.02107917e-03
7.14006782e-01 -4.54979420e-01 9.17021394e-01 4.19420689e-01
-5.99299431e-01 9.08351839e-01 7.03490674e-02 -1.04697034e-01
-8.65609527e-01 -1.34570956e+00 -4.34281379e-01 -5.90433121e-01
-4.42292839e-02 4.72232401e-01 3.16027373e-01 -8.88286531... | [5.834269046783447, 3.807438850402832] |
b516335e-575b-4960-9db6-dc3d2fcb07ce | ecgbert-understanding-hidden-language-of-ecgs | 2306.06340 | null | https://arxiv.org/abs/2306.06340v1 | https://arxiv.org/pdf/2306.06340v1.pdf | ECGBERT: Understanding Hidden Language of ECGs with Self-Supervised Representation Learning | In the medical field, current ECG signal analysis approaches rely on supervised deep neural networks trained for specific tasks that require substantial amounts of labeled data. However, our paper introduces ECGBERT, a self-supervised representation learning approach that unlocks the underlying language of ECGs. By uns... | ['Fatemeh Afghah', 'Fatemeh Khadem', 'Haben G. Yhdego', 'Phillip Si', 'Sajad Mousavi', 'Seokmin Choi'] | 2023-06-10 | null | null | null | null | ['arrhythmia-detection', 'sleep-apnea-detection', 'heartbeat-classification', 'unsupervised-pre-training'] | ['medical', 'medical', 'medical', 'methodology'] | [ 4.40605819e-01 3.28817219e-01 -1.82125792e-01 -6.51861012e-01
-9.79867458e-01 -4.15473133e-01 -6.56199604e-02 6.84689462e-01
-5.04938304e-01 5.77772439e-01 3.47799510e-01 -6.06939971e-01
-4.05786224e-02 -3.96362394e-01 -1.87797919e-01 -2.09189594e-01
-4.90081936e-01 4.01124835e-01 -4.04640108e-01 -8.59515667... | [14.372573852539062, 3.3714871406555176] |
dfd6311d-471d-4260-90e7-e4092415f714 | evaluation-and-comparison-of-eight-popular | 2208.02063 | null | https://arxiv.org/abs/2208.02063v1 | https://arxiv.org/pdf/2208.02063v1.pdf | Evaluation and comparison of eight popular Lidar and Visual SLAM algorithms | In this paper, we evaluate eight popular and open-source 3D Lidar and visual SLAM (Simultaneous Localization and Mapping) algorithms, namely LOAM, Lego LOAM, LIO SAM, HDL Graph, ORB SLAM3, Basalt VIO, and SVO2. We have devised experiments both indoor and outdoor to investigate the effect of the following items: i) effe... | ['Reza Ghabcheloo', 'Nataliya Strokina', 'Bharath Garigipati'] | 2022-08-03 | null | null | null | null | ['simultaneous-localization-and-mapping'] | ['computer-vision'] | [-2.22948849e-01 -6.37910664e-01 -2.49461476e-02 -2.91324943e-01
-2.60961622e-01 -6.03579462e-01 6.85700595e-01 2.52202511e-01
-6.11801207e-01 1.15115857e+00 -1.41248301e-01 -3.21034908e-01
-4.14644569e-01 -8.19328189e-01 -4.56373841e-01 -2.73194969e-01
-3.53992313e-01 8.50665271e-01 5.38288057e-01 -3.67141843... | [7.356385231018066, -2.0439932346343994] |
368e528c-7e36-4b1d-9965-a13ebf205a35 | federated-few-shot-learning | 2306.10234 | null | https://arxiv.org/abs/2306.10234v3 | https://arxiv.org/pdf/2306.10234v3.pdf | Federated Few-shot Learning | Federated Learning (FL) enables multiple clients to collaboratively learn a machine learning model without exchanging their own local data. In this way, the server can exploit the computational power of all clients and train the model on a larger set of data samples among all clients. Although such a mechanism is prove... | ['Jundong Li', 'Huiyuan Chen', 'Chen Chen', 'Kaize Ding', 'Xingbo Fu', 'Song Wang'] | 2023-06-17 | null | null | null | null | ['few-shot-learning'] | ['methodology'] | [-1.67940319e-01 -3.53221029e-01 -4.87457871e-01 -3.21962863e-01
-9.20005500e-01 -3.10666829e-01 4.90569532e-01 -2.55748779e-01
-9.57427323e-02 6.15747988e-01 2.45420009e-01 -2.62860693e-02
-5.43545000e-02 -8.17951620e-01 -6.86480343e-01 -8.36629689e-01
3.06323946e-01 3.14492017e-01 3.44318986e-01 8.37717298... | [5.817436218261719, 6.291619300842285] |
af96146a-edd6-4900-a28d-00e3f8708127 | s-dccrn-super-wide-band-dccrn-with-learnable | 2111.08387 | null | https://arxiv.org/abs/2111.08387v1 | https://arxiv.org/pdf/2111.08387v1.pdf | S-DCCRN: Super Wide Band DCCRN with learnable complex feature for speech enhancement | In speech enhancement, complex neural network has shown promising performance due to their effectiveness in processing complex-valued spectrum. Most of the recent speech enhancement approaches mainly focus on wide-band signal with a sampling rate of 16K Hz. However, research on super wide band (e.g., 32K Hz) or even fu... | ['Tao Yu', 'Yannan Wang', 'Jun Huang', 'Lei Xie', 'Jiayao Sun', 'Mengtao Xing', 'Yihui Fu', 'Shubo Lv'] | 2021-11-16 | null | null | null | null | ['speech-denoising'] | ['speech'] | [ 3.17867219e-01 -6.18197843e-02 1.38962120e-01 -2.07045525e-01
-8.48527491e-01 -5.06273881e-02 2.39503458e-01 -1.30001247e-01
-5.85102856e-01 4.20937985e-01 5.82274199e-01 -2.62584955e-01
-1.03399895e-01 -5.59114158e-01 -4.77833629e-01 -7.07184792e-01
-3.43416780e-02 -5.58316767e-01 -7.08995536e-02 -5.33369839... | [14.969880104064941, 5.971793174743652] |
f94b9c69-0f93-44ec-9268-5a920c5d57dc | neuralrecon-real-time-coherent-3d | 2104.00681 | null | https://arxiv.org/abs/2104.00681v1 | https://arxiv.org/pdf/2104.00681v1.pdf | NeuralRecon: Real-Time Coherent 3D Reconstruction from Monocular Video | We present a novel framework named NeuralRecon for real-time 3D scene reconstruction from a monocular video. Unlike previous methods that estimate single-view depth maps separately on each key-frame and fuse them later, we propose to directly reconstruct local surfaces represented as sparse TSDF volumes for each video ... | ['Hujun Bao', 'Xiaowei Zhou', 'Linghao Chen', 'Yiming Xie', 'Jiaming Sun'] | 2021-04-01 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Sun_NeuralRecon_Real-Time_Coherent_3D_Reconstruction_From_Monocular_Video_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Sun_NeuralRecon_Real-Time_Coherent_3D_Reconstruction_From_Monocular_Video_CVPR_2021_paper.pdf | cvpr-2021-1 | ['3d-scene-reconstruction'] | ['computer-vision'] | [ 2.59176552e-01 -1.13497920e-01 1.80412024e-01 -5.34776270e-01
-5.74832320e-01 -1.89684764e-01 3.27071518e-01 -4.72041011e-01
-1.12916373e-01 3.90199482e-01 3.40206288e-02 1.18696857e-02
6.55482709e-02 -9.84589577e-01 -1.01389563e+00 -2.81715184e-01
3.29856598e-03 5.38279057e-01 4.78025287e-01 7.59992450... | [8.69855785369873, -2.8062498569488525] |
4ce4e89e-4bf0-4356-861a-030ef129e1a0 | revisiting-the-adversarial-robustness | 2204.07373 | null | https://arxiv.org/abs/2204.07373v2 | https://arxiv.org/pdf/2204.07373v2.pdf | Revisiting the Adversarial Robustness-Accuracy Tradeoff in Robot Learning | Adversarial training (i.e., training on adversarially perturbed input data) is a well-studied method for making neural networks robust to potential adversarial attacks during inference. However, the improved robustness does not come for free but rather is accompanied by a decrease in overall model accuracy and performa... | ['Thomas A. Henzinger', 'Daniela Rus', 'Alexander Amini', 'Mathias Lechner'] | 2022-04-15 | null | null | null | null | ['gesture-recognition'] | ['computer-vision'] | [ 5.46196103e-01 5.41924119e-01 7.53961056e-02 -3.46158177e-01
-1.08521986e+00 -7.64006972e-01 8.29198539e-01 -2.70078838e-01
-5.70777893e-01 8.50809932e-01 -2.76499778e-01 -5.07458568e-01
2.44625919e-02 -6.46656752e-01 -1.28670084e+00 -7.66264558e-01
-5.25611043e-01 2.67494202e-01 2.12280914e-01 -4.62985516... | [5.535090446472168, 7.852194786071777] |
16d39a24-6e94-4812-a324-56368262c35c | sulcal-pattern-matching-with-the-wasserstein | 2307.00385 | null | https://arxiv.org/abs/2307.00385v1 | https://arxiv.org/pdf/2307.00385v1.pdf | Sulcal Pattern Matching with the Wasserstein Distance | We present the unified computational framework for modeling the sulcal patterns of human brain obtained from the magnetic resonance images. The Wasserstein distance is used to align the sulcal patterns nonlinearly. These patterns are topologically different across subjects making the pattern matching a challenge. We wo... | ['Moo K. Chung', 'Soumya Das', 'Zijian Chen'] | 2023-07-01 | null | null | null | null | ['image-registration'] | ['computer-vision'] | [-1.09962426e-01 1.67801782e-01 7.81154446e-03 -6.93889558e-01
-3.11091002e-02 -5.01370251e-01 6.20909870e-01 -2.48313501e-01
-4.89287704e-01 3.48201901e-01 3.31153959e-01 2.61771884e-02
-2.65854806e-01 -3.30400914e-01 -3.96509975e-01 -5.47321022e-01
-7.55534232e-01 4.44086462e-01 7.27027208e-02 -3.02599967... | [13.970860481262207, -2.5228018760681152] |
f96d3b8e-7777-49be-ab4c-2ec32c06e18b | towards-the-universal-defense-for-query-based | 2304.10088 | null | https://arxiv.org/abs/2304.10088v1 | https://arxiv.org/pdf/2304.10088v1.pdf | Towards the Universal Defense for Query-Based Audio Adversarial Attacks | Recently, studies show that deep learning-based automatic speech recognition (ASR) systems are vulnerable to adversarial examples (AEs), which add a small amount of noise to the original audio examples. These AE attacks pose new challenges to deep learning security and have raised significant concerns about deploying A... | ['Lei Ju', 'Yuxuan Chen', 'Zheng Sun', 'Feng Guo'] | 2023-04-20 | null | null | null | null | ['audio-fingerprint'] | ['audio'] | [ 3.63662362e-01 -2.50819743e-01 9.42481980e-02 1.49033172e-02
-1.24868286e+00 -1.09948647e+00 4.22433108e-01 -2.46436484e-02
-1.69133678e-01 2.58048326e-01 -2.19329298e-02 -6.95023894e-01
-5.93443699e-02 -7.07135677e-01 -7.13344514e-01 -6.18372560e-01
-3.36790562e-01 -3.41615118e-02 1.58906281e-01 -3.19492996... | [13.979853630065918, 5.817153453826904] |
305d7217-32e3-4f8e-b574-7c0d70c1c136 | image-retrieval-with-a-bayesian-model-of | 1603.09522 | null | http://arxiv.org/abs/1603.09522v1 | http://arxiv.org/pdf/1603.09522v1.pdf | Image Retrieval with a Bayesian Model of Relevance Feedback | A content-based image retrieval system based on multinomial relevance
feedback is proposed. The system relies on an interactive search paradigm where
at each round a user is presented with k images and selects the one closest to
their ideal target. Two approaches, one based on the Dirichlet distribution and
one based t... | ['Shawe-Taylor John', 'Teh Yee Whye', 'Glowacka Dorota'] | 2016-03-31 | null | null | null | null | ['content-based-image-retrieval'] | ['computer-vision'] | [ 1.57936081e-01 -1.51186541e-01 -2.69364893e-01 -3.85151088e-01
-7.23832190e-01 -5.05918741e-01 7.02380836e-01 2.84934729e-01
-1.05649388e+00 5.17430067e-01 -7.32151493e-02 -2.52941579e-01
-5.00164926e-01 -4.97491986e-01 -2.91641615e-02 -8.82347643e-01
2.21739300e-02 8.54622066e-01 5.49113035e-01 -1.34696737... | [10.752792358398438, 0.11138852685689926] |
658c0fc0-7756-43e6-b5d6-1cce6e5717d8 | generate-to-understand-for-representation | 2306.10056 | null | https://arxiv.org/abs/2306.10056v1 | https://arxiv.org/pdf/2306.10056v1.pdf | Generate to Understand for Representation | In recent years, a significant number of high-quality pretrained models have emerged, greatly impacting Natural Language Understanding (NLU), Natural Language Generation (NLG), and Text Representation tasks. Traditionally, these models are pretrained on custom domain corpora and finetuned for specific tasks, resulting ... | ['Xiaoqing Liu', 'Xiande Zhong', 'Changshang Xue'] | 2023-06-14 | null | null | null | null | ['contrastive-learning', 'contrastive-learning', 'text-generation'] | ['computer-vision', 'methodology', 'natural-language-processing'] | [ 2.87475199e-01 -1.70995191e-01 -6.22006357e-01 -3.37372541e-01
-1.38552701e+00 -6.43706679e-01 1.01543224e+00 3.33709747e-01
-7.16367364e-01 7.02514112e-01 4.52236950e-01 -3.57223392e-01
4.05675948e-01 -7.09847152e-01 -5.89069486e-01 -2.66913533e-01
2.42913023e-01 8.16915512e-01 3.31717916e-02 -2.39518836... | [11.190160751342773, 8.113241195678711] |
447b1a6a-c3ec-49b9-b939-d472e2ace985 | persistence-curves-a-canonical-framework-for | 1904.07768 | null | https://arxiv.org/abs/1904.07768v4 | https://arxiv.org/pdf/1904.07768v4.pdf | Persistence Curves: A canonical framework for summarizing persistence diagrams | Persistence diagrams are one of the main tools in the field of Topological Data Analysis (TDA). They contain fruitful information about the shape of data. The use of machine learning algorithms on the space of persistence diagrams proves to be challenging as the space lacks an inner product. For that reason, transformi... | ['Yu-Min Chung', 'Austin Lawson'] | 2019-04-16 | null | null | null | null | ['texture-classification'] | ['computer-vision'] | [ 1.57654971e-01 -5.02188385e-01 -4.39341873e-01 1.16883136e-01
-1.06480472e-01 -7.03408122e-01 9.15191114e-01 5.71036875e-01
8.67307484e-02 6.14313126e-01 -1.56284362e-01 -6.23290181e-01
-6.42978311e-01 -7.74499774e-01 -6.26935124e-01 -1.12520027e+00
-4.23538506e-01 2.91989625e-01 4.62006599e-01 -6.00404382... | [7.472250461578369, 4.182532787322998] |
2b159917-cdae-4d5b-b315-03d430d902ac | iterative-deep-graph-learning-for-graph | null | null | https://openreview.net/forum?id=Bkl2UlrFwr | https://openreview.net/pdf?id=Bkl2UlrFwr | Iterative Deep Graph Learning for Graph Neural Networks | In this paper, we propose an end-to-end graph learning framework, namely Iterative Deep Graph Learning (IDGL), for jointly learning graph structure and graph embedding simultaneously. We first cast graph structure learning problem as similarity metric learning problem and leverage an adapted graph regularization for co... | ['Mohammed J. Zaki', 'Lingfei Wu', 'Yu Chen'] | 2019-09-25 | null | null | null | null | ['graph-structure-learning'] | ['graphs'] | [ 6.39925450e-02 4.52426344e-01 -4.64297593e-01 -3.66371036e-01
-6.78766787e-01 -5.41992068e-01 6.29630864e-01 3.96152765e-01
-1.58010483e-01 4.36382085e-01 3.07588708e-02 -4.59030986e-01
-7.02076852e-02 -9.13795710e-01 -7.98139930e-01 -4.00466383e-01
-3.27550441e-01 4.34345752e-01 -1.05390977e-02 1.82113916... | [7.142280578613281, 6.259690761566162] |
6dc404ab-0153-48c0-a861-a7a87f52cc07 | counting-motifs-with-graph-sampling | 1802.07773 | null | http://arxiv.org/abs/1802.07773v1 | http://arxiv.org/pdf/1802.07773v1.pdf | Counting Motifs with Graph Sampling | Applied researchers often construct a network from a random sample of nodes
in order to infer properties of the parent network. Two of the most widely used
sampling schemes are subgraph sampling, where we sample each vertex
independently with probability $p$ and observe the subgraph induced by the
sampled vertices, and... | ['Jason M. Klusowski', 'Yihong Wu'] | 2018-02-21 | null | null | null | null | ['graph-sampling'] | ['graphs'] | [ 4.37662393e-01 5.52928507e-01 -2.05763340e-01 8.69509671e-03
-6.01143897e-01 -6.73769355e-01 -4.69772667e-02 2.58701414e-01
-3.68972331e-01 9.61815417e-01 -7.04562485e-01 -5.24466872e-01
-6.74424350e-01 -1.29713511e+00 -9.42342877e-01 -9.10489500e-01
-8.68616700e-01 6.29305363e-01 4.36804980e-01 9.06146988... | [6.630346775054932, 4.889187812805176] |
576dab8d-7806-44b1-bfd7-b687631fa374 | high-accuracy-malware-classification-with-a | 2004.05258 | null | https://arxiv.org/abs/2004.05258v2 | https://arxiv.org/pdf/2004.05258v2.pdf | Exploring Optimal Deep Learning Models for Image-based Malware Variant Classification | Analyzing a huge amount of malware is a major burden for security analysts. Since emerging malware is often a variant of existing malware, automatically classifying malware into known families greatly reduces a part of their burden. Image-based malware classification with deep learning is an attractive approach for its... | ['Takahiro Shinagawa', 'Rikima Mitsuhashi'] | 2020-04-10 | null | null | null | null | ['computer-security'] | ['miscellaneous'] | [-1.89877614e-01 -7.20556319e-01 -4.17958170e-01 -8.85690302e-02
-1.69084325e-01 -6.59273684e-01 8.09129357e-01 -2.67934829e-01
-5.47091246e-01 6.16286278e-01 -4.78891999e-01 -7.11857617e-01
7.52771571e-02 -6.47535682e-01 -8.20815623e-01 -5.76720834e-01
-4.98610288e-01 4.71066028e-01 2.30355456e-01 -3.18199873... | [14.412548065185547, 9.673664093017578] |
01b24dca-f721-403e-821d-993dd9826e45 | improvement-of-computational-performance-of | 2301.05102 | null | https://arxiv.org/abs/2301.05102v1 | https://arxiv.org/pdf/2301.05102v1.pdf | Improvement of Computational Performance of Evolutionary AutoML in a Heterogeneous Environment | Resource-intensive computations are a major factor that limits the effectiveness of automated machine learning solutions. In the paper, we propose a modular approach that can be used to increase the quality of evolutionary optimization for modelling pipelines with a graph-based structure. It consists of several stages ... | ['Denis Nasonov', 'Sergey Pakulin', 'Valerii Pokrovskii', 'Sergey Teryoshkin', 'Nikolay O. Nikitin'] | 2023-01-12 | null | null | null | null | ['automl'] | ['methodology'] | [-1.08931974e-01 -3.65817212e-02 3.18833321e-01 -2.00911552e-01
8.42541605e-02 -2.95425951e-01 5.32197118e-01 5.10053039e-01
-5.25390327e-01 6.09491885e-01 -1.88453868e-01 -2.63235509e-01
-4.37804312e-01 -9.85274613e-01 -3.12809020e-01 -4.52016085e-01
-4.46880087e-02 6.39357448e-01 6.37800097e-01 -2.54489392... | [6.052398204803467, 3.6042537689208984] |
9f4d3b94-bed4-47ae-864b-8cb9ad0e6791 | diffusion-models-for-constrained-domains | 2304.05364 | null | https://arxiv.org/abs/2304.05364v1 | https://arxiv.org/pdf/2304.05364v1.pdf | Diffusion Models for Constrained Domains | Denoising diffusion models are a recent class of generative models which achieve state-of-the-art results in many domains such as unconditional image generation and text-to-speech tasks. They consist of a noising process destroying the data and a backward stage defined as the time-reversal of the noising diffusion. Bui... | ['Michael Hutchinson', 'Emile Mathieu', 'Valentin De Bortoli', 'Leo Klarner', 'Nic Fishman'] | 2023-04-11 | null | null | null | null | ['protein-design'] | ['medical'] | [ 4.04954463e-01 2.44370863e-01 2.22325251e-01 -2.22463936e-01
-5.37416488e-02 -6.72876298e-01 1.00149417e+00 -1.96006820e-01
-4.87029940e-01 3.93477410e-01 6.43342808e-02 -5.20010531e-01
-3.67817611e-01 -7.67780781e-01 -6.72130585e-01 -1.16634631e+00
-5.92077784e-02 2.23367780e-01 2.67884284e-01 -5.03352821... | [7.391412734985352, 3.8652255535125732] |
0304d1cb-74b6-4ca9-83c5-a0f474e0e4d6 | layered-embeddings-for-amodal-instance | 2002.06264 | null | https://arxiv.org/abs/2002.06264v1 | https://arxiv.org/pdf/2002.06264v1.pdf | Layered Embeddings for Amodal Instance Segmentation | The proposed method extends upon the representational output of semantic instance segmentation by explicitly including both visible and occluded parts. A fully convolutional network is trained to produce consistent pixel-level embedding across two layers such that, when clustered, the results convey the full spatial ex... | ['Lance Pérez', 'Yanfeng Liu', 'Eric Psota'] | 2020-02-14 | null | null | null | null | ['amodal-instance-segmentation'] | ['computer-vision'] | [-2.65803952e-02 4.26446140e-01 -3.30991000e-01 -6.74300790e-01
-7.58707464e-01 -4.38112974e-01 3.38736713e-01 9.16274861e-02
-1.85035795e-01 4.44328725e-01 2.04035968e-01 -8.86293873e-02
2.79813796e-01 -8.24353397e-01 -8.27592909e-01 -2.60498405e-01
2.12160870e-02 9.11210626e-02 3.20336789e-01 2.68953383... | [8.073039054870605, -3.1100800037384033] |
61cd3f77-6a54-45e1-a4b3-2790416ea5ee | open-world-semi-supervised-novel-class | 2305.13095 | null | https://arxiv.org/abs/2305.13095v1 | https://arxiv.org/pdf/2305.13095v1.pdf | Open-world Semi-supervised Novel Class Discovery | Traditional semi-supervised learning tasks assume that both labeled and unlabeled data follow the same class distribution, but the realistic open-world scenarios are of more complexity with unknown novel classes mixed in the unlabeled set. Therefore, it is of great challenge to not only recognize samples from known cla... | ['Junming Shao', 'Qinli Yang', 'Yulu Fan', 'Tongze Zhang', 'Yangqiming Wang', 'Jiaming Liu'] | 2023-05-22 | null | null | null | null | ['novel-class-discovery', 'novel-class-discovery'] | ['computer-vision', 'methodology'] | [ 2.81618655e-01 -1.11781597e-01 -3.20866972e-01 -5.70811629e-01
-6.88448429e-01 -5.71802139e-01 5.78827977e-01 3.29074144e-01
-1.59321800e-01 6.91667855e-01 -1.38690367e-01 2.12270111e-01
-2.60887802e-01 -5.89544773e-01 -3.16309363e-01 -9.24992204e-01
-1.41424136e-02 6.21119738e-01 3.65887552e-01 2.50866354... | [9.731759071350098, 2.9180076122283936] |
ab4a210d-1f12-4129-b47b-d4679c077549 | low-rankness-of-complex-valued-spectrogram | 1903.05603 | null | http://arxiv.org/abs/1903.05603v1 | http://arxiv.org/pdf/1903.05603v1.pdf | Low-rankness of Complex-valued Spectrogram and Its Application to Phase-aware Audio Processing | Low-rankness of amplitude spectrograms has been effectively utilized in audio
signal processing methods including non-negative matrix factorization. However,
such methods have a fundamental limitation owing to their amplitude-only
treatment where the phase of the observed signal is utilized for resynthesizing
the estim... | [] | 2019-03-13 | null | null | null | null | ['audio-signal-processing', 'audio-denoising'] | ['audio', 'audio'] | [ 5.89773417e-01 -6.87038302e-02 1.78675070e-01 1.31510451e-01
-8.79119992e-01 -6.02118254e-01 5.25160059e-02 -1.25462160e-01
-2.04828292e-01 6.99221432e-01 4.75026071e-01 5.52972332e-02
-4.25792158e-01 -2.33688191e-01 -4.27429616e-01 -8.97307575e-01
-2.67389596e-01 -4.17500943e-01 -3.22689384e-01 -2.55679309... | [15.432947158813477, 5.585911750793457] |
ff77f46e-be16-4e18-8974-63fdb7ea7dc3 | temporally-coherent-embeddings-for-self | 2004.02753 | null | https://arxiv.org/abs/2004.02753v5 | https://arxiv.org/pdf/2004.02753v5.pdf | Temporally Coherent Embeddings for Self-Supervised Video Representation Learning | This paper presents TCE: Temporally Coherent Embeddings for self-supervised video representation learning. The proposed method exploits inherent structure of unlabeled video data to explicitly enforce temporal coherency in the embedding space, rather than indirectly learning it through ranking or predictive proxy tasks... | ['Olivia Mackenzie-Ross', 'Peyman Moghadam', 'Joshua Knights', 'Daniel Ward', 'Ben Harwood', 'Anthony Vanderkop'] | 2020-03-21 | null | null | null | null | ['self-supervised-action-recognition'] | ['computer-vision'] | [-1.40875429e-01 -4.47701216e-02 -5.38661838e-01 -3.06606919e-01
-3.77366722e-01 -6.18614018e-01 6.88027620e-01 -1.46826245e-02
-3.69877458e-01 5.62904954e-01 5.41681290e-01 1.77366495e-01
3.18213254e-02 -5.02059281e-01 -1.08599293e+00 -6.47381604e-01
-5.53224325e-01 1.60960108e-01 4.13578004e-01 -7.74357244... | [8.675714492797852, 0.727519154548645] |
4edf22ea-c80d-4394-b375-302067c7ad12 | solution-of-debertav3-on-commonsenseqa | 2206.05033 | null | https://arxiv.org/abs/2206.05033v2 | https://arxiv.org/pdf/2206.05033v2.pdf | Solution of DeBERTaV3 on CommonsenseQA | We report the performance of DeBERTaV3 on CommonsenseQA in this report. We simply formalize the answer selection as a text classification for DeBERTaV3. The strong natural language inference ability of DeBERTaV3 helps its single and ensemble model set the new (w/o external knowledge) state-of-the-art on CommonsenseQA. | ['Hai Zhao', 'Zuchao Li', 'Letian Peng'] | 2022-04-30 | null | null | null | null | ['answer-selection'] | ['natural-language-processing'] | [-3.18715721e-01 4.64467525e-01 -1.75941423e-01 -4.51974392e-01
-6.39252424e-01 -8.99547756e-01 6.13856494e-01 3.06619108e-01
-2.17584893e-01 1.14023280e+00 4.20327902e-01 -8.71844292e-01
-2.42181849e-02 -1.31601954e+00 -6.36874616e-01 6.64979815e-02
5.40127695e-01 9.61592197e-01 6.17397130e-01 -1.17150402... | [10.08289623260498, 8.03609848022461] |
cdc34961-cf50-4758-9609-f6bc43a1f03e | event-based-moving-object-detection-and | 1803.04523 | null | https://arxiv.org/abs/1803.04523v3 | https://arxiv.org/pdf/1803.04523v3.pdf | Event-based Moving Object Detection and Tracking | Event-based vision sensors, such as the Dynamic Vision Sensor (DVS), are ideally suited for real-time motion analysis. The unique properties encompassed in the readings of such sensors provide high temporal resolution, superior sensitivity to light and low latency. These properties provide the grounds to estimate motio... | ['Yiannis Aloimonos', 'Chethan Parameshwara', 'Cornelia Fermuller', 'Anton Mitrokhin'] | 2018-03-12 | null | null | null | null | ['motion-detection', 'moving-object-detection', 'event-based-vision'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 1.41512439e-01 -6.90965295e-01 2.19176844e-01 9.84684527e-02
-6.14569709e-02 -6.09876096e-01 7.30185688e-01 -1.27798580e-02
-6.66459322e-01 5.50763011e-01 -2.49810845e-01 9.25179794e-02
2.71557886e-02 -6.04829252e-01 -4.26143080e-01 -7.19444811e-01
1.18490942e-02 1.97311312e-01 8.85483503e-01 1.10379800... | [8.575807571411133, -1.3782639503479004] |
fec7425a-4b16-4708-9a8a-db21fec76aca | gfnet-geometric-flow-network-for-3d-point | 2207.02605 | null | https://arxiv.org/abs/2207.02605v2 | https://arxiv.org/pdf/2207.02605v2.pdf | GFNet: Geometric Flow Network for 3D Point Cloud Semantic Segmentation | Point cloud semantic segmentation from projected views, such as range-view (RV) and bird's-eye-view (BEV), has been intensively investigated. Different views capture different information of point clouds and thus are complementary to each other. However, recent projection-based methods for point cloud semantic segmenta... | ['DaCheng Tao', 'Baosheng Yu', 'Haibo Qiu'] | 2022-07-06 | null | null | null | null | ['robust-3d-semantic-segmentation', 'lidar-semantic-segmentation'] | ['computer-vision', 'computer-vision'] | [-5.27251624e-02 -1.69062793e-01 5.17312139e-02 -5.88655114e-01
-5.00439346e-01 -6.81841671e-01 4.98551220e-01 -2.11452767e-01
1.14673346e-01 3.09500583e-02 -7.42811561e-02 -2.58593589e-01
2.48249415e-02 -1.04239988e+00 -8.96600664e-01 -3.71831745e-01
4.65248227e-01 5.49645543e-01 5.20283759e-01 -2.16023296... | [8.200126647949219, -3.021498203277588] |
07849698-9add-4bd1-be93-b202e62b2dfe | feddct-a-dynamic-cross-tier-federated | 2307.04420 | null | https://arxiv.org/abs/2307.04420v1 | https://arxiv.org/pdf/2307.04420v1.pdf | FedDCT: A Dynamic Cross-Tier Federated Learning Scheme in Wireless Communication Networks | With the rapid proliferation of Internet of Things (IoT) devices and the growing concern for data privacy among the public, Federated Learning (FL) has gained significant attention as a privacy-preserving machine learning paradigm. FL enables the training of a global model among clients without exposing local data. How... | ['Dongcheng Li', 'Jianyong Jiang', 'Lianghaojie Zhou', 'Xiaoyun Gan', 'Chuanjian Yao', 'Youquan Xian', 'Peng Liu'] | 2023-07-10 | null | null | null | null | ['federated-learning'] | ['methodology'] | [-2.14485019e-01 -4.88897562e-02 -6.29577041e-01 -7.06321836e-01
-4.34069246e-01 -7.39040852e-01 4.35112268e-02 -9.56451371e-02
-2.97070265e-01 6.95113540e-01 3.83999236e-02 -5.55092394e-01
-4.19131935e-01 -9.49404538e-01 -4.41949010e-01 -8.94153893e-01
-1.92094687e-02 1.82987452e-01 2.34087899e-01 5.98630130... | [5.838403224945068, 6.242898941040039] |
b51d2cd4-67ab-48c6-a327-e0ff562c90a3 | occformer-dual-path-transformer-for-vision | 2304.05316 | null | https://arxiv.org/abs/2304.05316v1 | https://arxiv.org/pdf/2304.05316v1.pdf | OccFormer: Dual-path Transformer for Vision-based 3D Semantic Occupancy Prediction | The vision-based perception for autonomous driving has undergone a transformation from the bird-eye-view (BEV) representations to the 3D semantic occupancy. Compared with the BEV planes, the 3D semantic occupancy further provides structural information along the vertical direction. This paper presents OccFormer, a dual... | ['Dalong Du', 'Zheng Zhu', 'Yunpeng Zhang'] | 2023-04-11 | null | null | null | null | ['lidar-semantic-segmentation'] | ['computer-vision'] | [ 2.76735008e-01 1.82579741e-01 -1.24410771e-01 -7.64473617e-01
-6.54482484e-01 -3.42861235e-01 5.02216756e-01 -1.94860116e-01
-1.52089223e-01 6.30381703e-02 3.46421272e-01 -1.99555531e-01
1.03584724e-02 -1.00585485e+00 -9.37717259e-01 -5.74244022e-01
4.63245243e-01 5.87198496e-01 4.44370508e-01 5.09069152... | [8.308125495910645, -2.700193166732788] |
84529901-7709-48ba-bda7-adc5424363cc | rediscovery-of-the-effectiveness-of-standard | 2204.01209 | null | https://arxiv.org/abs/2204.01209v2 | https://arxiv.org/pdf/2204.01209v2.pdf | EResFD: Rediscovery of the Effectiveness of Standard Convolution for Lightweight Face Detection | This paper analyses the design choices of face detection architecture that improve efficiency between computation cost and accuracy. Specifically, we re-examine the effectiveness of the standard convolutional block as a lightweight backbone architecture on face detection. Unlike the current tendency of lightweight arch... | ['Youngjoon Yoo', 'Joonsang Yu', 'Beomyoung Kim', 'JoonHyun Jeong'] | 2022-04-04 | null | null | null | null | ['face-detection'] | ['computer-vision'] | [-2.71463066e-01 1.45274043e-01 6.88481703e-03 -3.95191759e-01
-4.55231220e-03 -1.99870452e-01 2.57381111e-01 -5.73605597e-01
-5.39386392e-01 3.31327260e-01 -2.66225487e-01 -4.60818797e-01
1.30595505e-01 -9.78625417e-01 -5.44711769e-01 -5.23119330e-01
-1.59932822e-01 -1.46268636e-01 2.71065533e-01 -9.87330526... | [13.29586410522461, 0.6910514831542969] |
57dea243-0114-4ae6-96cf-6595da0b87f9 | approximate-bisimulation-relations-for-neural | 2202.01214 | null | https://arxiv.org/abs/2202.01214v1 | https://arxiv.org/pdf/2202.01214v1.pdf | Approximate Bisimulation Relations for Neural Networks and Application to Assured Neural Network Compression | In this paper, we propose a concept of approximate bisimulation relation for feedforward neural networks. In the framework of approximate bisimulation relation, a novel neural network merging method is developed to compute the approximate bisimulation error between two neural networks based on reachability analysis of ... | ['Zhongzhu Shao', 'Weiming Xiang'] | 2022-02-02 | null | null | null | null | ['neural-network-compression', 'neural-network-compression'] | ['methodology', 'miscellaneous'] | [ 2.55918652e-01 6.91304728e-02 1.61892418e-02 -2.65113562e-01
2.60352582e-01 -3.76333535e-01 2.52204031e-01 2.16846019e-01
-1.71013907e-01 4.21355188e-01 -6.41098738e-01 -5.60076535e-01
-4.94886845e-01 -9.11447108e-01 -1.20703316e+00 -1.63303867e-01
2.40617678e-01 3.02582830e-01 7.14969710e-02 -9.53480378... | [8.30261516571045, 3.1552932262420654] |
d07def8e-1faf-4a76-80ea-1d172fcf0e72 | gnowee-a-hybrid-metaheuristic-optimization | 1804.05429 | null | http://arxiv.org/abs/1804.05429v1 | http://arxiv.org/pdf/1804.05429v1.pdf | Gnowee: A Hybrid Metaheuristic Optimization Algorithm for Constrained, Black Box, Combinatorial Mixed-Integer Design | This paper introduces Gnowee, a modular, Python-based, open-source hybrid
metaheuristic optimization algorithm (Available from
https://github.com/SlaybaughLab/Gnowee). Gnowee is designed for rapid
convergence to nearly globally optimum solutions for complex, constrained
nuclear engineering problems with mixed-integer a... | ['James Bevins', 'Rachel Slaybaugh'] | 2018-04-15 | null | null | null | null | ['metaheuristic-optimization'] | ['methodology'] | [ 1.60992920e-01 -3.21256995e-01 -2.15294629e-01 -1.63781960e-02
-7.12231159e-01 -5.63222349e-01 -1.56203723e-02 6.67039528e-02
-1.99646950e-01 1.10321152e+00 -8.11546221e-02 -3.47060353e-01
-8.92788947e-01 -6.76842272e-01 -1.76345587e-01 -1.16778207e+00
5.02607226e-02 8.39652658e-01 -1.08621135e-01 -4.63626474... | [5.754472732543945, 3.6067540645599365] |
a3b640d4-4d25-46cc-ac00-b0346ddca207 | robust-speech-recognition-using-generative | 1711.01567 | null | http://arxiv.org/abs/1711.01567v1 | http://arxiv.org/pdf/1711.01567v1.pdf | Robust Speech Recognition Using Generative Adversarial Networks | This paper describes a general, scalable, end-to-end framework that uses the
generative adversarial network (GAN) objective to enable robust speech
recognition. Encoders trained with the proposed approach enjoy improved
invariance by learning to map noisy audio to the same embedding space as that
of clean audio. Unlike... | ['Anuroop Sriram', 'Yashesh Gaur', 'Heewoo Jun', 'Sanjeev Satheesh'] | 2017-11-05 | null | null | null | null | ['robust-speech-recognition'] | ['speech'] | [ 4.40081328e-01 1.34874657e-01 3.82595688e-01 -4.53431100e-01
-1.36232364e+00 -5.95843911e-01 7.86435425e-01 -6.79980874e-01
-4.28694546e-01 8.00572336e-01 4.35734779e-01 -3.56774867e-01
4.10633758e-02 -4.28770453e-01 -9.09316301e-01 -7.73880243e-01
-8.94793421e-02 -1.10259302e-01 4.22958881e-02 -2.62995809... | [15.035205841064453, 6.229610443115234] |
73f22ecb-bd13-41bf-8ee6-e5171053ea06 | cross-lingual-alignment-vs-joint-training-a | 1910.04708 | null | https://arxiv.org/abs/1910.04708v4 | https://arxiv.org/pdf/1910.04708v4.pdf | Cross-lingual Alignment vs Joint Training: A Comparative Study and A Simple Unified Framework | Learning multilingual representations of text has proven a successful method for many cross-lingual transfer learning tasks. There are two main paradigms for learning such representations: (1) alignment, which maps different independently trained monolingual representations into a shared space, and (2) joint training, ... | ['Zirui Wang', 'Jiateng Xie', 'Jaime Carbonell', 'Yiming Yang', 'Ruochen Xu', 'Graham Neubig'] | 2019-10-10 | cross-lingual-alignment-vs-joint-training-a-1 | https://openreview.net/forum?id=S1l-C0NtwS | https://openreview.net/pdf?id=S1l-C0NtwS | iclr-2020-1 | ['cross-lingual-ner'] | ['natural-language-processing'] | [-3.94235440e-02 -1.60950705e-01 -7.19496965e-01 -4.81716543e-01
-1.52133012e+00 -8.79321218e-01 1.04032779e+00 -6.37519136e-02
-4.89617527e-01 9.51181412e-01 4.92699146e-01 -6.03756607e-01
1.31894037e-01 -4.07955438e-01 -9.20227468e-01 -5.09612322e-01
2.28077799e-01 6.06221199e-01 -1.66948751e-01 -4.18020993... | [11.040057182312012, 9.933225631713867] |
69deff87-10f5-41f0-a09c-7d4e471fca3a | odoviz-a-3d-odometry-visualization-and | 2107.07557 | null | https://arxiv.org/abs/2107.07557v1 | https://arxiv.org/pdf/2107.07557v1.pdf | OdoViz: A 3D Odometry Visualization and Processing Tool | OdoViz is a reactive web-based tool for 3D visualization and processing of autonomous vehicle datasets designed to support common tasks in visual place recognition research. The system includes functionality for loading, inspecting, visualizing, and processing GPS/INS poses, point clouds and camera images. It supports ... | ['John McDonald', 'Saravanabalagi Ramachandran'] | 2021-07-15 | null | null | null | null | ['visual-place-recognition'] | ['computer-vision'] | [-4.02630001e-01 -4.08338249e-01 7.64494389e-02 -6.27425730e-01
-5.59358954e-01 -8.84227455e-01 5.59095442e-01 3.63552481e-01
-3.23739260e-01 7.74172023e-02 -4.49488573e-02 -8.87582541e-01
1.71451852e-01 -7.94537246e-01 -4.14635986e-01 -4.70777333e-01
-1.50807396e-01 5.20199239e-01 7.93201208e-01 -4.07565117... | [7.677425384521484, -1.5377899408340454] |
d4d45b6c-760e-4472-aa3f-2e3fb34263ad | unsupervised-text-style-transfer-via | 1901.11333 | null | https://arxiv.org/abs/1901.11333v4 | https://arxiv.org/pdf/1901.11333v4.pdf | IMaT: Unsupervised Text Attribute Transfer via Iterative Matching and Translation | Text attribute transfer aims to automatically rewrite sentences such that they possess certain linguistic attributes, while simultaneously preserving their semantic content. This task remains challenging due to a lack of supervised parallel data. Existing approaches try to explicitly disentangle content and attribute i... | ['Jonas Mueller', 'Enrico Santus', 'Zhijing Jin', 'Nicholas Matthews', 'Di Jin'] | 2019-01-31 | imat-unsupervised-text-attribute-transfer-via | https://aclanthology.org/D19-1306 | https://aclanthology.org/D19-1306.pdf | ijcnlp-2019-11 | ['text-attribute-transfer'] | ['natural-language-processing'] | [ 7.93587208e-01 2.92328417e-01 -2.06236780e-01 -5.96224010e-01
-1.21939862e+00 -9.82647955e-01 7.69812107e-01 2.73700923e-01
-4.81554389e-01 9.79077339e-01 4.37561959e-01 -1.68833986e-01
2.53151417e-01 -6.17487788e-01 -8.47010553e-01 -2.73938507e-01
5.97673714e-01 1.00109339e+00 4.00857106e-02 -4.94656205... | [11.575116157531738, 9.531254768371582] |
2de1e46c-f346-46f8-8b2f-448b29670841 | context-generation-improves-open-domain | 2210.06349 | null | https://arxiv.org/abs/2210.06349v2 | https://arxiv.org/pdf/2210.06349v2.pdf | Context Generation Improves Open Domain Question Answering | Closed-book question answering (QA) requires a model to directly answer an open-domain question without access to any external knowledge. Prior work on closed-book QA either directly finetunes or prompts a pretrained language model (LM) to leverage the stored knowledge. However, they do not fully exploit the parameteri... | ['Bryan Catanzaro', 'Anima Anandkumar', 'Pascale Fung', 'Mohammad Shoeybi', 'Ryan Prenger', 'Peng Xu', 'Shrimai Prabhumoye', 'Mostofa Patwary', 'Dan Su'] | 2022-10-12 | null | null | null | null | ['open-domain-question-answering'] | ['natural-language-processing'] | [ 2.79862404e-01 5.07437408e-01 4.60489132e-02 -5.39882302e-01
-1.77547598e+00 -1.11930251e+00 5.59378982e-01 1.47444710e-01
-5.50875187e-01 7.72120357e-01 2.12374717e-01 -5.73931694e-01
-1.04004800e-01 -1.04254746e+00 -9.19451833e-01 -7.63967857e-02
6.32099748e-01 8.91122937e-01 8.77390206e-01 -5.51372111... | [11.10256290435791, 7.942512512207031] |
e014c029-6ead-4726-8dbf-93ee20f8af5f | dink-net-neural-clustering-on-large-graphs | 2305.18405 | null | https://arxiv.org/abs/2305.18405v2 | https://arxiv.org/pdf/2305.18405v2.pdf | Dink-Net: Neural Clustering on Large Graphs | Deep graph clustering, which aims to group the nodes of a graph into disjoint clusters with deep neural networks, has achieved promising progress in recent years. However, the existing methods fail to scale to the large graph with million nodes. To solve this problem, a scalable deep graph clustering method (Dink-Net) ... | ['Stan Z. Li', 'Xinwang Liu', 'Xihong Yang', 'Sihang Zhou', 'Jun Xia', 'Ke Liang', 'Yue Liu'] | 2023-05-28 | null | null | null | null | ['graph-clustering'] | ['graphs'] | [-4.43895638e-01 1.27887085e-01 -1.20525248e-01 -4.75603610e-01
-6.97525442e-01 -6.48099184e-01 1.58718407e-01 9.52517241e-02
-2.42938787e-01 3.96199107e-01 -1.62031069e-01 -2.68199414e-01
-1.12401411e-01 -8.77488554e-01 -7.78595507e-01 -8.71781647e-01
-2.16772795e-01 5.99294782e-01 -2.83382982e-01 1.82738945... | [7.337675094604492, 6.028116226196289] |
6f8b211c-3bb6-4a23-8b26-c5bfefdf5a47 | synctalkface-talking-face-generation-with | 2211.00924 | null | https://arxiv.org/abs/2211.00924v2 | https://arxiv.org/pdf/2211.00924v2.pdf | SyncTalkFace: Talking Face Generation with Precise Lip-Syncing via Audio-Lip Memory | The challenge of talking face generation from speech lies in aligning two different modal information, audio and video, such that the mouth region corresponds to input audio. Previous methods either exploit audio-visual representation learning or leverage intermediate structural information such as landmarks and 3D mod... | ['Yong Man Ro', 'Jeongsoo Choi', 'Joanna Hong', 'Minsu Kim', 'Se Jin Park'] | 2022-11-02 | null | null | null | null | ['audio-visual-synchronization', 'audio-visual-synchronization', 'talking-face-generation', 'face-generation'] | ['audio', 'computer-vision', 'computer-vision', 'computer-vision'] | [-3.20110954e-02 -3.12668160e-02 -5.62712431e-01 4.59182113e-02
-1.02091169e+00 -5.41559339e-01 5.14147043e-01 -2.08468974e-01
2.53342092e-01 3.68284822e-01 6.64798737e-01 3.16914439e-01
3.28676552e-01 -4.82807368e-01 -7.28536367e-01 -6.50407791e-01
2.26074859e-01 1.13649398e-01 2.42293626e-01 9.57755893... | [13.236777305603027, -0.40611204504966736] |
55d87a84-1785-4d8d-bd1d-71feedbd978b | octave-deep-plane-sweeping-network-reducing | null | null | https://ieeexplore.ieee.org/document/8867874 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8867874 | Octave Deep Plane-Sweeping Network: Reducing Spatial Redundancy for Learning-Based Plane-Sweeping Stereo | In this paper, we propose the octave deep plane-sweeping network (OctDPSNet). OctDPSNet is a novel learning-based plane-sweeping stereo, which drastically reduces the required GPU memory and computation time while achieving a state-of-the-art depth estimation accuracy. Inspired by octave convolution, we divide image fe... | ['R. Komatsu', 'H. Asama', 'Y. Tamura', 'H. Fujii', 'A. Yamashita'] | 2019-10-14 | null | null | null | ieee-access-2019-10 | ['stereo-depth-estimation'] | ['computer-vision'] | [-6.62205219e-02 -2.15045467e-01 1.23177961e-01 -4.32508379e-01
-5.39270163e-01 -1.25209987e-01 5.00130296e-01 1.18087418e-01
-6.43131256e-01 4.83216435e-01 7.28357881e-02 1.85395237e-02
2.56150663e-02 -1.30454051e+00 -8.72039139e-01 -6.13105059e-01
1.97048366e-01 1.58294424e-01 5.91326296e-01 1.08408127... | [8.84377384185791, -2.4800033569335938] |
c9450109-2b8a-4b71-af5a-3a6c4ada0bb5 | biphasic-learning-of-gans-for-high-resolution | 1904.06624 | null | http://arxiv.org/abs/1904.06624v1 | http://arxiv.org/pdf/1904.06624v1.pdf | Biphasic Learning of GANs for High-Resolution Image-to-Image Translation | Despite that the performance of image-to-image translation has been
significantly improved by recent progress in generative models, current methods
still suffer from severe degradation in training stability and sample quality
when applied to the high-resolution situation. In this work, we present a novel
training frame... | ['Huaibo Huang', 'Yi Li', 'Jingtuo Liu', 'Zhenan Sun', 'Jie Cao', 'Ran He'] | 2019-04-14 | null | null | null | null | ['mutual-information-estimation'] | ['methodology'] | [ 6.85471535e-01 -1.42815694e-01 -1.08258761e-01 -2.77223229e-01
-1.10216010e+00 -4.27083254e-01 6.16631746e-01 -6.81269169e-01
-2.92072892e-02 9.06949341e-01 -3.72082628e-02 3.85181941e-02
6.73957616e-02 -6.31669521e-01 -7.64038861e-01 -7.89350450e-01
3.41508150e-01 -8.38594213e-02 -1.25248030e-01 8.75466689... | [11.731344223022461, -0.5497217774391174] |
0fef911e-d3c1-4f25-875b-3baeb5fc5c5b | rethinking-two-consensuses-of-the | 2212.00399 | null | https://arxiv.org/abs/2212.00399v1 | https://arxiv.org/pdf/2212.00399v1.pdf | Rethinking Two Consensuses of the Transferability in Deep Learning | Deep transfer learning (DTL) has formed a long-term quest toward enabling deep neural networks (DNNs) to reuse historical experiences as efficiently as humans. This ability is named knowledge transferability. A commonly used paradigm for DTL is firstly learning general knowledge (pre-training) and then reusing (fine-tu... | ['Li Liu', 'Chris Ding', 'Jingxian Li', 'Yixiong Chen'] | 2022-12-01 | null | null | null | null | ['general-knowledge'] | ['miscellaneous'] | [ 7.24684820e-02 3.26568969e-02 -8.86818171e-02 -3.98356527e-01
-1.11723086e-02 -6.80413604e-01 7.81572163e-01 -1.07717961e-01
-7.81427324e-01 1.12201309e+00 -1.10586286e-01 -1.30955443e-01
-3.54750425e-01 -9.98176038e-01 -7.87819564e-01 -7.92569458e-01
1.00013964e-01 6.37973174e-02 5.60066402e-01 -4.87129629... | [9.86319637298584, 2.93998646736145] |
187d88c6-3fc9-47bc-a060-31eabd5ff118 | step-by-step-loss-goes-very-far-multi-step | 2302.05120 | null | https://arxiv.org/abs/2302.05120v1 | https://arxiv.org/pdf/2302.05120v1.pdf | Step by Step Loss Goes Very Far: Multi-Step Quantization for Adversarial Text Attacks | We propose a novel gradient-based attack against transformer-based language models that searches for an adversarial example in a continuous space of token probabilities. Our algorithm mitigates the gap between adversarial loss for continuous and discrete text representations by performing multi-step quantization in a q... | ['Klaudia Bałazy', 'Piotr Gaiński'] | 2023-02-10 | null | null | null | null | ['adversarial-text'] | ['adversarial'] | [ 2.74004996e-01 -4.34108265e-02 -2.59523749e-01 -3.22672427e-01
-1.69276524e+00 -7.81729698e-01 8.99076283e-01 3.70767742e-01
-7.24499106e-01 5.63533127e-01 1.29354283e-01 -7.37107873e-01
4.83900845e-01 -8.78577232e-01 -7.86366820e-01 -3.31303120e-01
3.80690396e-02 4.23195451e-01 3.16977680e-01 -3.04257780... | [6.0290327072143555, 8.088109970092773] |
5ef682dd-af47-465d-9596-11c8471b5f85 | learning-the-regularization-in-dce-mr-image | 2109.07548 | null | https://arxiv.org/abs/2109.07548v2 | https://arxiv.org/pdf/2109.07548v2.pdf | Learning the Regularization in DCE-MR Image Reconstruction for Functional Imaging of Kidneys | Kidney DCE-MRI aims at both qualitative assessment of kidney anatomy and quantitative assessment of kidney function by estimating the tracer kinetic (TK) model parameters. Accurate estimation of TK model parameters requires an accurate measurement of the arterial input function (AIF) with high temporal resolution. Acce... | ['Sila Kurugol', 'Onur Afacan', 'Cemre Ariyurek', 'Aziz Koçanaoğulları'] | 2021-09-15 | null | null | null | null | ['kidney-function'] | ['medical'] | [ 4.35042650e-01 -2.04913169e-02 -3.39531898e-02 -5.29330194e-01
-5.90735912e-01 -3.06650609e-01 1.17416747e-01 1.66817322e-01
-3.91546994e-01 7.76896000e-01 3.44823658e-01 9.47269723e-02
-2.98438728e-01 -5.65441191e-01 -6.51830435e-01 -8.38482857e-01
-2.30194330e-01 2.75423646e-01 2.97167189e-02 2.92135835... | [13.596352577209473, -2.512986898422241] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.