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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
a4111641-4d18-4dbd-8371-8d2fbf3316e3 | is-that-a-chair-imagining-affordances-using | 1909.07572 | null | https://arxiv.org/abs/1909.07572v2 | https://arxiv.org/pdf/1909.07572v2.pdf | Is That a Chair? Imagining Affordances Using Simulations of an Articulated Human Body | For robots to exhibit a high level of intelligence in the real world, they must be able to assess objects for which they have no prior knowledge. Therefore, it is crucial for robots to perceive object affordances by reasoning about physical interactions with the object. In this paper, we propose a novel method to provi... | ['Gregory S. Chirikjian', 'Hongtao Wu', 'Deven Misra'] | 2019-09-17 | null | null | null | null | ['physical-simulations'] | ['miscellaneous'] | [ 1.15121566e-01 4.79079396e-01 2.48702168e-01 -4.39396143e-01
2.41370071e-02 -5.69388866e-01 6.16597950e-01 -1.62366480e-01
-1.86154664e-01 3.63115788e-01 -4.10847038e-01 -2.31309310e-01
-5.08861467e-02 -7.50798941e-01 -1.08736718e+00 -5.88602602e-01
4.85092252e-02 8.20482135e-01 1.95593163e-01 -1.69111609... | [5.853400707244873, -0.8138607740402222] |
ef5fe8f1-b8ea-4d35-a569-a87a1e83da4c | fast-vehicle-detection-algorithm-based-on | 2304.06002 | null | https://arxiv.org/abs/2304.06002v3 | https://arxiv.org/pdf/2304.06002v3.pdf | Fast vehicle detection algorithm based on lightweight YOLO7-tiny | The swift and precise detection of vehicles plays a significant role in intelligent transportation systems. Current vehicle detection algorithms encounter challenges of high computational complexity, low detection rate, and limited feasibility on mobile devices. To address these issues, this paper proposes a lightweigh... | ['Fei Zhong', 'Hao Xu', 'Yihua Chen', 'Bo Li'] | 2023-04-12 | null | null | null | null | ['fast-vehicle-detection'] | ['computer-vision'] | [-2.73329526e-01 -3.95474583e-01 2.11858302e-02 -2.06098974e-01
-1.59982517e-01 -2.02946946e-01 4.86364305e-01 -3.47598255e-01
-7.63538599e-01 2.58170724e-01 -2.77467340e-01 -2.72394240e-01
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-3.54179665e-02 -1.37288466e-01 9.79118049e-01 -1.39589399... | [8.130840301513672, -0.9521154165267944] |
f36c9bc1-ec46-48fa-87c9-9bb75b7a116c | optimal-sizing-of-a-holdout-set-for-safe | 2202.06374 | null | https://arxiv.org/abs/2202.06374v3 | https://arxiv.org/pdf/2202.06374v3.pdf | Optimal sizing of a holdout set for safe predictive model updating | Predictive risk scores are increasingly used to guide clinical or other interventions in complex settings, particularly healthcare. Directly updating a risk score used to guide interventions leads to biased risk estimates. We propose updating using a `holdout set' -- a subset of the population that does not receive ris... | ['James Liley', 'Louis J M Aslett', 'Samuel R Emerson', 'Sami Haidar-Wehbe'] | 2022-02-13 | null | null | null | null | ['holdout-set'] | ['computer-vision'] | [ 4.70879465e-01 6.23617589e-01 -6.95481896e-01 -5.62319577e-01
-9.21843529e-01 -3.89438659e-01 2.70308852e-01 7.33720422e-01
-4.91847962e-01 8.94833446e-01 1.88399285e-01 -7.40749478e-01
-7.37790942e-01 -8.76008987e-01 -5.24202108e-01 -5.63782215e-01
-6.06547058e-01 9.82139051e-01 1.64581627e-01 2.56873757... | [8.078789710998535, 5.1588873863220215] |
851dcb8c-4082-4976-b8e9-7c72edcd9f85 | multi-view-response-selection-for-human | null | null | https://aclanthology.org/D16-1036 | https://aclanthology.org/D16-1036.pdf | Multi-view Response Selection for Human-Computer Conversation | null | ['dianhai yu', 'daxiang dong', 'Hua Wu', 'Hao Tian', 'Xuan Liu', 'Xiangyang Zhou', 'Shiqi Zhao', 'Rui Yan'] | 2016-11-01 | null | null | null | emnlp-2016-11 | ['conversational-response-selection'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.442988872528076, 3.6425631046295166] |
6b7f8476-738b-4605-bd1f-9c99e9d785b0 | genie-show-me-the-data-for-quantization | 2212.0478 | null | https://arxiv.org/abs/2212.04780v2 | https://arxiv.org/pdf/2212.04780v2.pdf | Genie: Show Me the Data for Quantization | Zero-shot quantization is a promising approach for developing lightweight deep neural networks when data is inaccessible owing to various reasons, including cost and issues related to privacy. By exploiting the learned parameters ($\mu$ and $\sigma$) of batch normalization layers in an FP32-pre-trained model, zero-shot... | ['Ho-young Kim', 'Chungman Lee', 'Yongkweon Jeon'] | 2022-12-09 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Jeon_Genie_Show_Me_the_Data_for_Quantization_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Jeon_Genie_Show_Me_the_Data_for_Quantization_CVPR_2023_paper.pdf | cvpr-2023-1 | ['data-free-quantization', 'data-free-quantization'] | ['computer-vision', 'methodology'] | [ 1.43145248e-01 4.36129063e-01 -1.44350544e-01 -6.21169448e-01
-9.87501323e-01 -1.58216693e-02 4.63604510e-01 -8.05990323e-02
-8.12967002e-01 8.78229141e-01 -1.32455349e-01 -1.95094600e-01
3.24499398e-01 -1.06366169e+00 -9.80859101e-01 -5.99149764e-01
2.89399683e-01 1.67385250e-01 -1.13879256e-02 -1.94522068... | [8.824516296386719, 2.9724390506744385] |
f42054a2-3f54-472a-be20-147c5971ff07 | siamese-based-neural-network-for-offline | 2211.14443 | null | https://arxiv.org/abs/2211.14443v1 | https://arxiv.org/pdf/2211.14443v1.pdf | Siamese based Neural Network for Offline Writer Identification on word level data | Handwriting recognition is one of the desirable attributes of document comprehension and analysis. It is concerned with the documents writing style and characteristics that distinguish the authors. The diversity of text images, notably in images with varying handwriting, makes the process of learning good features diff... | ['Suresh Sundaram', 'Vineet Kumar'] | 2022-11-17 | null | null | null | null | ['handwriting-recognition'] | ['computer-vision'] | [ 4.1286069e-01 -5.7098991e-01 -2.1627247e-01 -3.8684238e-02
-2.8468826e-01 -6.8418050e-01 7.9183930e-01 2.0630789e-01
-2.8067163e-01 2.9907221e-01 3.4565401e-01 3.6467737e-01
-4.0265614e-01 -6.1442715e-01 -4.2794830e-01 -8.5966176e-01
2.7995232e-01 3.2065105e-01 2.1966466e-01 -2.0678252e-01
1.0066280e+00... | [11.786808967590332, 2.244600534439087] |
5b9e983d-28d0-4013-b0b0-2469287f5856 | nuwa-infinity-autoregressive-over | 2207.09814 | null | https://arxiv.org/abs/2207.09814v2 | https://arxiv.org/pdf/2207.09814v2.pdf | NUWA-Infinity: Autoregressive over Autoregressive Generation for Infinite Visual Synthesis | In this paper, we present NUWA-Infinity, a generative model for infinite visual synthesis, which is defined as the task of generating arbitrarily-sized high-resolution images or long-duration videos. An autoregressive over autoregressive generation mechanism is proposed to deal with this variable-size generation task, ... | ['Nan Duan', 'Yuejian Fang', 'Zicheng Liu', 'Lijuan Wang', 'JianFeng Wang', 'Zhe Gan', 'Xiaowei Hu', 'Jian Liang', 'Chenfei Wu'] | 2022-07-20 | null | null | null | null | ['image-outpainting', 'video-generation'] | ['computer-vision', 'computer-vision'] | [-8.91575292e-02 -1.15018830e-01 -1.28043205e-01 9.20791477e-02
-8.20767641e-01 -3.88666987e-01 5.57227850e-01 -3.34485620e-01
5.52087091e-02 5.89954615e-01 3.53027582e-01 -2.20086932e-01
1.06316730e-01 -1.11576116e+00 -8.90212893e-01 -6.39918506e-01
-4.56619896e-02 1.92876339e-01 2.89751679e-01 -1.25520304... | [11.043061256408691, -0.6491833925247192] |
862cd549-e986-471e-b8bc-2fd8f730a6fa | a-non-linear-function-on-function-model-for | 2011.12378 | null | https://arxiv.org/abs/2011.12378v1 | https://arxiv.org/pdf/2011.12378v1.pdf | A Non-linear Function-on-Function Model for Regression with Time Series Data | In the last few decades, building regression models for non-scalar variables, including time series, text, image, and video, has attracted increasing interests of researchers from the data analytic community. In this paper, we focus on a multivariate time series regression problem. Specifically, we aim to learn mathema... | ['Hamed Khorasgani', 'Aniruddha Rajendra Rao', 'Chetan Gupta', 'HaiYan Wang', 'Qiyao Wang'] | 2020-11-24 | null | null | null | null | ['time-series-regression'] | ['time-series'] | [ 2.17673838e-01 -6.57491088e-01 -3.33162487e-01 -2.65050411e-01
-6.09230757e-01 -2.82590896e-01 3.86711299e-01 3.70747715e-01
-5.77307701e-01 1.05459940e+00 -3.08229387e-01 -1.98221937e-01
-6.21562362e-01 -7.29220510e-01 -6.91123784e-01 -8.07344615e-01
-4.01360363e-01 -1.58033878e-01 -1.94134012e-01 -1.33839175... | [7.049724578857422, 3.198007822036743] |
ae68a171-e3ac-4023-94b6-4d5383fe4c23 | text-based-person-search-via-attribute-aided | null | null | https://openaccess.thecvf.com/content_WACV_2020/html/Aggarwal_Text-based_Person_Search_via_Attribute-aided_Matching_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Aggarwal_Text-based_Person_Search_via_Attribute-aided_Matching_WACV_2020_paper.pdf | Text-based Person Search via Attribute-aided Matching | Text-based person search aims to retrieve the pedestrian images that best match a given text query. Existing
methods utilize class-id information to get discriminative
and identity-preserving features. However, it is not wellexplored whether it is beneficial to explicitly ensure that the
semantics of the data are re... | ['Surbhi Aggarwal R.', 'Anirban Chakraborty', 'Venkatesh Babu'] | 2020-03-14 | null | null | null | null | ['nlp-based-person-retrival', 'person-search'] | ['computer-vision', 'computer-vision'] | [ 2.00340256e-01 -3.62806648e-01 -4.45730865e-01 -7.36265898e-01
-8.67185414e-01 -3.32591355e-01 8.90094519e-01 2.46110588e-01
-7.19501972e-01 7.27534115e-01 5.38763762e-01 1.09243132e-01
-2.24405274e-01 -7.15617001e-01 -4.95730996e-01 -6.86966360e-01
3.39589357e-01 6.29705489e-01 6.38374016e-02 -3.87635222... | [14.621710777282715, 0.9325137734413147] |
29b15e2b-9d05-43b8-85b1-3261f9aa508a | sk-unet-model-with-fourier-domain-for-mitosis | 2109.00957 | null | https://arxiv.org/abs/2109.00957v3 | https://arxiv.org/pdf/2109.00957v3.pdf | Sk-Unet Model with Fourier Domain for Mitosis Detection | Mitotic count is the most important morphological feature of breast cancer grading. Many deep learning-based methods have been proposed but suffer from domain shift. In this work, we construct a Fourier-based segmentation model for mitosis detection to address the problem. Swapping the low-frequency spectrum of source ... | ['Xiyue Wang', 'Jun Zhang', 'Feng Luo', 'Sen yang'] | 2021-09-01 | null | null | null | null | ['mitosis-detection'] | ['medical'] | [ 8.26527104e-02 2.24815514e-02 -2.94766992e-01 -3.03018391e-01
-9.59746122e-01 -2.28283405e-01 1.92798972e-01 3.07684834e-03
-6.39708817e-01 8.91891181e-01 -1.70411959e-01 -3.50473493e-01
6.63139522e-02 -8.92688155e-01 -3.19556236e-01 -1.12402165e+00
2.50322312e-01 2.97472686e-01 6.83890581e-01 7.71840215... | [15.083744049072266, -3.13897967338562] |
c5eba12f-63db-486c-980f-4c3f40af6d85 | oracle-teacher-towards-better-knowledge | 2111.03664 | null | https://arxiv.org/abs/2111.03664v3 | https://arxiv.org/pdf/2111.03664v3.pdf | Oracle Teacher: Leveraging Target Information for Better Knowledge Distillation of CTC Models | Knowledge distillation (KD), best known as an effective method for model compression, aims at transferring the knowledge of a bigger network (teacher) to a much smaller network (student). Conventional KD methods usually employ the teacher model trained in a supervised manner, where output labels are treated only as tar... | ['Nam Soo Kim', 'Sunghwan Ahn', 'Hyeonseung Lee', 'Hyung Yong Kim', 'Ji Won Yoon'] | 2021-11-05 | null | null | null | null | ['scene-text-recognition'] | ['computer-vision'] | [ 7.77151406e-01 3.51496369e-01 -5.65638840e-01 -2.95098096e-01
-6.73974395e-01 -6.18147433e-01 5.36795795e-01 -3.88202332e-02
-3.84995371e-01 5.29070795e-01 -2.52589941e-01 -6.42568529e-01
1.11232474e-01 -6.89416349e-01 -9.15090740e-01 -1.01644933e+00
2.94245869e-01 4.80976313e-01 3.80609810e-01 4.64405864... | [9.484235763549805, 3.27411150932312] |
ac5c723a-064a-4b95-806f-c94693dfc219 | easnet-searching-elastic-and-accurate-network | 2207.09796 | null | https://arxiv.org/abs/2207.09796v1 | https://arxiv.org/pdf/2207.09796v1.pdf | EASNet: Searching Elastic and Accurate Network Architecture for Stereo Matching | Recent advanced studies have spent considerable human efforts on optimizing network architectures for stereo matching but hardly achieved both high accuracy and fast inference speed. To ease the workload in network design, neural architecture search (NAS) has been applied with great success to various sparse prediction... | ['Xiaowen Chu', 'Kaiyong Zhao', 'Shaohuai Shi', 'Qiang Wang'] | 2022-07-20 | null | null | null | null | ['stereo-matching-1'] | ['computer-vision'] | [-2.27123827e-01 -4.91595179e-01 -3.72863740e-01 -5.48027694e-01
-1.12656973e-01 -2.02934265e-01 6.41997010e-02 -3.20281804e-01
-4.82264191e-01 4.05311227e-01 -8.05766657e-02 -5.64749658e-01
-8.47997069e-02 -8.29777300e-01 -7.17908204e-01 -3.51407498e-01
1.23487366e-02 5.70773184e-01 5.97188234e-01 1.89125855... | [8.648715019226074, 2.7015795707702637] |
bdc844ba-3c1d-42bc-8a6d-2776fe032e35 | morphological-inflection-with-phonological | 2306.12581 | null | https://arxiv.org/abs/2306.12581v1 | https://arxiv.org/pdf/2306.12581v1.pdf | Morphological Inflection with Phonological Features | Recent years have brought great advances into solving morphological tasks, mostly due to powerful neural models applied to various tasks as (re)inflection and analysis. Yet, such morphological tasks cannot be considered solved, especially when little training data is available or when generalizing to previously unseen ... | ['Reut Tsarfaty', 'Omer Goldman', 'David Guriel'] | 2023-06-21 | null | null | null | null | ['morphological-inflection'] | ['natural-language-processing'] | [ 2.39925221e-01 1.60844058e-01 3.99387814e-03 -4.10913259e-01
-6.58944786e-01 -1.02037776e+00 5.97607374e-01 4.77520674e-01
-7.15035796e-01 4.15086448e-01 6.25419319e-01 -6.92528129e-01
2.74331927e-01 -8.43204737e-01 -7.44080365e-01 -3.64774764e-01
5.50143309e-02 6.02003515e-01 2.45600015e-01 -3.72798771... | [10.648652076721191, 9.63415813446045] |
78a0721d-4004-4db7-8d3c-fb1ecb9d50bc | helping-domain-experts-build-speech | 1510.01942 | null | http://arxiv.org/abs/1510.01942v1 | http://arxiv.org/pdf/1510.01942v1.pdf | Helping Domain Experts Build Speech Translation Systems | We present a new platform, "Regulus Lite", which supports rapid development
and web deployment of several types of phrasal speech translation systems using
a minimal formalism. A distinguishing feature is that most development work can
be performed directly by domain experts. We motivate the need for platforms of
this ... | ['Pierrette Bouillon', 'Manny Rayner', 'Sonia Halimi', 'Irene Strasly', 'Alejandro Armando', 'Johanna Gerlach', 'Sarah Ebling', 'Nikos Tsourakis'] | 2015-10-07 | null | null | null | null | ['sign-language-translation'] | ['computer-vision'] | [-8.87190104e-02 5.22649527e-01 3.65146101e-02 -4.14911747e-01
-1.13886893e+00 -8.20530236e-01 4.23849195e-01 -2.29623675e-01
-2.57593870e-01 9.77846026e-01 4.66097355e-01 -7.99092591e-01
1.93590671e-01 -3.40255380e-01 -7.63340071e-02 -2.05290109e-01
6.12851977e-01 7.81771004e-01 5.94532967e-01 -7.78062165... | [14.319775581359863, 7.166370868682861] |
7e638b28-f87d-4e1b-b374-b00d4b5f4f6e | keep-meeting-summaries-on-topic-abstractive | null | null | https://aclanthology.org/P19-1210 | https://aclanthology.org/P19-1210.pdf | Keep Meeting Summaries on Topic: Abstractive Multi-Modal Meeting Summarization | Transcripts of natural, multi-person meetings differ significantly from documents like news articles, which can make Natural Language Generation models for generating summaries unfocused. We develop an abstractive meeting summarizer from both videos and audios of meeting recordings. Specifically, we propose a multi-mod... | ['Richard J. Radke', 'Manling Li', 'Lingyu Zhang', 'Heng Ji'] | 2019-07-01 | null | null | null | acl-2019-7 | ['meeting-summarization'] | ['natural-language-processing'] | [ 3.33460212e-01 2.94917524e-01 -1.44623086e-01 -3.69418651e-01
-1.33278656e+00 -6.13820791e-01 8.31491351e-01 5.90432286e-01
-1.06299303e-01 7.35747874e-01 1.30515599e+00 8.72719437e-02
2.40348667e-01 -2.31321216e-01 -5.07819712e-01 -4.06426221e-01
2.59819269e-01 2.34654397e-01 -7.64507577e-02 -4.22058776... | [12.598539352416992, 9.37134838104248] |
3353f4b7-00c8-41a4-bb8c-933719c04246 | multi-path-region-based-convolutional-neural | 1703.09145 | null | http://arxiv.org/abs/1703.09145v1 | http://arxiv.org/pdf/1703.09145v1.pdf | Multi-Path Region-Based Convolutional Neural Network for Accurate Detection of Unconstrained "Hard Faces" | Large-scale variations still pose a challenge in unconstrained face
detection. To the best of our knowledge, no current face detection algorithm
can detect a face as large as 800 x 800 pixels while simultaneously detecting
another one as small as 8 x 8 pixels within a single image with equally high
accuracy. We propose... | ['Yuguang Liu', 'Martin D. Levine'] | 2017-03-27 | null | null | null | null | ['robust-face-recognition'] | ['computer-vision'] | [ 3.15116227e-01 4.70829941e-02 -4.10295241e-02 -2.61367053e-01
-7.15679526e-01 -2.77666509e-01 3.92401189e-01 -5.47467411e-01
-3.63150030e-01 4.40365285e-01 -3.73164296e-01 -8.38776976e-02
2.92394668e-01 -8.33843827e-01 -7.13638544e-01 -7.43287802e-01
-1.22406706e-01 2.58154213e-01 4.87267792e-01 4.75989543... | [13.341094017028809, 0.6576257944107056] |
d3b0c4e0-856b-4d5f-b957-6a5cbec0eacc | multi-cue-vehicle-detection-for-semantic | 1907.01176 | null | https://arxiv.org/abs/1907.01176v1 | https://arxiv.org/pdf/1907.01176v1.pdf | Multi-Cue Vehicle Detection for Semantic Video Compression In Georegistered Aerial Videos | Detection of moving objects such as vehicles in videos acquired from an airborne camera is very useful for video analytics applications. Using fast low power algorithms for onboard moving object detection would also provide region of interest-based semantic information for scene content aware image compression. This wo... | ['Guna Seetharaman', 'Noor Al-Shakarji', 'Kannappan Palaniappan', 'Hadi Aliakbarpour', 'Filiz Bunyak'] | 2019-07-02 | null | null | null | null | ['moving-object-detection'] | ['computer-vision'] | [ 4.22422081e-01 -1.03010309e+00 -1.14650264e-01 -8.40507448e-03
-4.08510536e-01 -8.09089065e-01 3.41558307e-01 -1.16838329e-01
-4.52388197e-01 4.99461621e-01 -4.34474200e-01 -5.59842922e-02
-3.88716757e-01 -8.29614520e-01 -6.23641551e-01 -7.69307256e-01
-6.00442886e-01 -1.51321888e-01 7.27642953e-01 2.34502344... | [6.956936359405518, -1.8350589275360107] |
bc14ea7c-ce72-457c-9eda-ba997041a131 | 4d-attention-based-neural-network-for-eeg | 2101.05484 | null | https://arxiv.org/abs/2101.05484v1 | https://arxiv.org/pdf/2101.05484v1.pdf | 4D Attention-based Neural Network for EEG Emotion Recognition | Electroencephalograph (EEG) emotion recognition is a significant task in the brain-computer interface field. Although many deep learning methods are proposed recently, it is still challenging to make full use of the information contained in different domains of EEG signals. In this paper, we present a novel method, cal... | ['Quansheng Ren', 'Zhendi Chen', 'Bowen Xu', 'Mengwen Ye', 'Guowen Xiao'] | 2021-01-14 | null | null | null | null | ['eeg-emotion-recognition'] | ['miscellaneous'] | [-1.33008420e-01 -5.42817473e-01 3.34515303e-01 -4.87817645e-01
-2.84862489e-01 1.32791206e-01 -2.66654771e-02 -1.49576277e-01
-4.06906188e-01 6.08360827e-01 1.83696568e-01 5.61353043e-02
-1.47008717e-01 -3.49347174e-01 -3.46553981e-01 -7.04240859e-01
-4.19360936e-01 -2.27336958e-01 -7.58825392e-02 -2.15132162... | [13.135287284851074, 3.477200508117676] |
969cfdae-2291-4196-8d19-ae7597614e9f | defocus-blur-detection-via-depth-distillation | 2007.08113 | null | https://arxiv.org/abs/2007.08113v1 | https://arxiv.org/pdf/2007.08113v1.pdf | Defocus Blur Detection via Depth Distillation | Defocus Blur Detection(DBD) aims to separate in-focus and out-of-focus regions from a single image pixel-wisely. This task has been paid much attention since bokeh effects are widely used in digital cameras and smartphone photography. However, identifying obscure homogeneous regions and borderline transitions in partia... | ['Chi-Man Pun', 'Xiaodong Cun'] | 2020-07-16 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2044_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123580732.pdf | eccv-2020-8 | ['defocus-blur-detection'] | ['computer-vision'] | [ 2.07915857e-01 -1.90002397e-01 -1.23115070e-01 -4.64338928e-01
-5.51213384e-01 -3.06928366e-01 2.48158842e-01 -1.64628431e-01
-4.02832150e-01 7.41503716e-01 4.25978154e-01 -1.85652375e-01
2.20646009e-01 -6.17558002e-01 -7.52592027e-01 -8.52954924e-01
4.17621315e-01 -2.43873656e-01 6.80206895e-01 2.47740760... | [11.244257926940918, -2.744135618209839] |
79091156-dfe2-43c0-8ce8-2cce29a8da8a | robot-learning-with-sensorimotor-pre-training | 2306.10007 | null | https://arxiv.org/abs/2306.10007v1 | https://arxiv.org/pdf/2306.10007v1.pdf | Robot Learning with Sensorimotor Pre-training | We present a self-supervised sensorimotor pre-training approach for robotics. Our model, called RPT, is a Transformer that operates on sequences of sensorimotor tokens. Given a sequence of camera images, proprioceptive robot states, and past actions, we encode the interleaved sequence into tokens, mask out a random sub... | ['Jitendra Malik', 'Trevor Darrell', 'Ken Goldberg', 'Letian Fu', 'Baifeng Shi', 'Ilija Radosavovic'] | 2023-06-16 | null | null | null | null | ['motion-planning'] | ['robots'] | [ 3.83561164e-01 2.15600193e-01 -4.62565601e-01 -9.75263715e-02
-5.85849822e-01 -4.51102942e-01 8.11068952e-01 -2.15555787e-01
-2.64501065e-01 7.38733351e-01 4.28520381e-01 -2.24510193e-01
-8.74879360e-02 -5.32800019e-01 -1.45906460e+00 -6.01845443e-01
-4.94654626e-01 7.99050927e-01 2.43322894e-01 1.02958225... | [4.568708896636963, 0.7653839588165283] |
76036eb1-6eb6-4b42-ba0f-ef4aa1f24753 | policy-entropy-for-out-of-distribution | 2005.12069 | null | https://arxiv.org/abs/2005.12069v1 | https://arxiv.org/pdf/2005.12069v1.pdf | Policy Entropy for Out-of-Distribution Classification | One critical prerequisite for the deployment of reinforcement learning systems in the real world is the ability to reliably detect situations on which the agent was not trained. Such situations could lead to potential safety risks when wrong predictions lead to the execution of harmful actions. In this work, we propose... | ['Claudia Linnhoff-Popien', 'Robert Müller', 'Andreas Sedlmeier', 'Steffen Illium'] | 2020-05-25 | null | null | null | null | ['one-class-classifier'] | ['methodology'] | [ 7.38575161e-02 2.42672697e-01 -1.64839730e-01 -2.63920486e-01
-5.40335476e-01 -7.06983685e-01 9.90246594e-01 4.27349597e-01
-6.93882048e-01 9.89863992e-01 -3.15705985e-01 -6.07126236e-01
-5.13758548e-02 -8.71575654e-01 -8.51168334e-01 -5.58153629e-01
-3.42626840e-01 4.66623932e-01 5.90923548e-01 -1.14899330... | [4.497403621673584, 1.8888804912567139] |
06657c7e-1ecc-4a18-bbdc-293aa41b1fe5 | unbalanced-optimal-transport-a-unified-1 | 2307.02402 | null | https://arxiv.org/abs/2307.02402v1 | https://arxiv.org/pdf/2307.02402v1.pdf | Unbalanced Optimal Transport: A Unified Framework for Object Detection | During training, supervised object detection tries to correctly match the predicted bounding boxes and associated classification scores to the ground truth. This is essential to determine which predictions are to be pushed towards which solutions, or to be discarded. Popular matching strategies include matching to the ... | ['Luc van Gool', 'Tinne Tuytelaars', 'Marc Proesmans', 'Johan A. K. Suykens', 'Pierre-François De Plaen', 'Henri De Plaen'] | 2023-07-05 | unbalanced-optimal-transport-a-unified | http://openaccess.thecvf.com//content/CVPR2023/html/De_Plaen_Unbalanced_Optimal_Transport_A_Unified_Framework_for_Object_Detection_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/De_Plaen_Unbalanced_Optimal_Transport_A_Unified_Framework_for_Object_Detection_CVPR_2023_paper.pdf | cvpr-2023-1 | ['object-detection'] | ['computer-vision'] | [ 3.10106903e-01 1.09602008e-02 -1.10522874e-01 -2.94143587e-01
-8.28601122e-01 -4.00569171e-01 6.35730267e-01 6.09431088e-01
-5.44684887e-01 5.96980929e-01 -5.38399994e-01 -3.50506268e-02
-1.26307443e-01 -9.92457628e-01 -5.41080952e-01 -8.20617199e-01
-1.84854358e-01 9.37373400e-01 1.11388719e+00 -1.04526728... | [8.647500991821289, -0.7554355263710022] |
d1f6bba3-d37b-4742-9102-64f48ed9ccf4 | road-damage-detection-using-deep-ensemble | 2011.00728 | null | https://arxiv.org/abs/2011.00728v1 | https://arxiv.org/pdf/2011.00728v1.pdf | Road Damage Detection using Deep Ensemble Learning | Road damage detection is critical for the maintenance of a road, which traditionally has been performed using expensive high-performance sensors. With the recent advances in technology, especially in computer vision, it is now possible to detect and categorize different types of road damages, which can facilitate effic... | ['Yasin Yilmaz', 'Keval Doshi'] | 2020-10-30 | null | null | null | null | ['road-damage-detection'] | ['computer-vision'] | [-9.43962187e-02 -2.41495416e-01 3.38702261e-01 -9.40710679e-03
-8.39946449e-01 -2.24722356e-01 4.94665414e-01 1.47955537e-01
-3.57608736e-01 5.39007366e-01 3.99060920e-02 -1.76964313e-01
-3.29876691e-01 -1.10004938e+00 -4.72279161e-01 -6.35030329e-01
-5.53950407e-02 1.59795210e-01 6.73177421e-01 -2.86033869... | [7.399096488952637, 1.177903413772583] |
f110e2d6-32aa-4215-80ed-66d4c82e18b5 | resolving-camera-position-for-a-practical | 2201.02946 | null | https://arxiv.org/abs/2201.02946v2 | https://arxiv.org/pdf/2201.02946v2.pdf | Resolving Camera Position for a Practical Application of Gaze Estimation on Edge Devices | Most Gaze estimation research only works on a setup condition that a camera perfectly captures eyes gaze. They have not literarily specified how to set up a camera correctly for a given position of a person. In this paper, we carry out a study on gaze estimation with a logical camera setup position. We further bring ou... | ['Moongu Jeon', 'Tin Trung Tran', 'Linh Van Ma'] | 2022-01-09 | null | null | null | null | ['gaze-estimation'] | ['computer-vision'] | [-9.70811248e-02 -2.87339211e-01 -1.14846542e-01 -5.31404436e-01
-8.12750161e-02 -1.49416804e-01 -1.63152236e-02 -3.03884000e-01
-3.23279440e-01 3.90580922e-01 -2.92885482e-01 -3.10482144e-01
1.96873292e-01 -3.45363468e-01 -5.32903075e-01 -4.94841397e-01
3.64151478e-01 9.76727754e-02 2.05294043e-01 -5.79331070... | [14.102879524230957, 0.1253758817911148] |
df261ae5-832c-4d97-aaf4-e539b63f5c45 | image-quality-aware-diagnosis-via-meta | 2303.15038 | null | https://arxiv.org/abs/2303.15038v2 | https://arxiv.org/pdf/2303.15038v2.pdf | Image Quality-aware Diagnosis via Meta-knowledge Co-embedding | Medical images usually suffer from image degradation in clinical practice, leading to decreased performance of deep learning-based models. To resolve this problem, most previous works have focused on filtering out degradation-causing low-quality images while ignoring their potential value for models. Through effectivel... | ['Hao Chen', 'Siyu Chen', 'Haoxuan Che'] | 2023-03-27 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Che_Image_Quality-Aware_Diagnosis_via_Meta-Knowledge_Co-Embedding_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Che_Image_Quality-Aware_Diagnosis_via_Meta-Knowledge_Co-Embedding_CVPR_2023_paper.pdf | cvpr-2023-1 | ['image-quality-assessment'] | ['computer-vision'] | [ 2.88456589e-01 1.05441205e-01 -4.50073063e-01 -3.98679674e-01
-7.44466424e-01 5.80991954e-02 2.06405699e-01 3.98106053e-02
-1.29289970e-01 4.50344533e-01 4.72001195e-01 -7.67238885e-02
-4.49907929e-01 -7.34268248e-01 -4.31117266e-01 -7.73742318e-01
-7.38288835e-03 -1.06281981e-01 4.51080613e-02 1.23257600... | [14.536144256591797, -2.1756839752197266] |
414aeda5-9d61-4f7c-8d5c-ac749e51d880 | data-augmentation-with-manifold-exploring | 1901.0442 | null | http://arxiv.org/abs/1901.04420v1 | http://arxiv.org/pdf/1901.04420v1.pdf | Data Augmentation with Manifold Exploring Geometric Transformations for Increased Performance and Robustness | In this paper we propose a novel augmentation technique that improves not
only the performance of deep neural networks on clean test data, but also
significantly increases their robustness to random transformations, both affine
and projective. Inspired by ManiFool, the augmentation is performed by a
line-search manifol... | ['Christian Wachinger', 'Rüdiger Göbl', 'Muhammad Ferjad Naeem', 'Walter Simson', 'Magdalini Paschali', 'Nassir Navab', 'Abhijit Guha Roy'] | 2019-01-14 | null | null | null | null | ['skin-lesion-classification'] | ['medical'] | [ 5.79273582e-01 7.16558218e-01 -1.28466235e-02 -2.10010543e-01
-6.44914567e-01 -5.25881469e-01 5.42914331e-01 -1.75586149e-01
-1.87112153e-01 5.15380323e-01 -2.48092830e-01 -4.15449083e-01
-5.99906817e-02 -8.88062000e-01 -1.10371256e+00 -8.64879072e-01
-2.18854733e-02 3.97706211e-01 -6.11624680e-02 -1.39978379... | [5.597176551818848, 7.881606101989746] |
8e3da1ed-d4b9-49a5-bdcc-9cf7ef619326 | les-syst-emes-de-dialogue-orient-es-but-etat | null | null | https://aclanthology.org/2019.jeptalnrecital-recital.7 | https://aclanthology.org/2019.jeptalnrecital-recital.7.pdf | Les syst\`emes de dialogue orient\'es-but : \'etat de l'art et perspectives d'am\'elioration (Goal-oriented dialog systems : a recent overview and research prospects ) | La gestion et la s{\'e}lection des informations pertinentes pour un tour de parole donn{\'e} restent un probl{\`e}me pour les syst{\`e}mes de dialogue {\`a} domaine ouvert. Pour ces derniers, les interactions possibles entre un utilisateur et un agent sont a priori infinies et ind{\'e}finies. La possibilit{\'e} d{'}une... | ["L{\\'e}on-Paul Schaub", 'Cyndel Vaudapiviz'] | 2019-07-01 | null | null | null | jeptalnrecital-2019-7 | ['goal-oriented-dialog'] | ['natural-language-processing'] | [ 6.32489622e-02 4.58816469e-01 5.69701552e-01 -3.38247538e-01
-4.08470035e-01 -1.23862052e+00 7.97195435e-01 3.69697273e-01
-7.74491370e-01 8.99567425e-01 -3.95102382e-01 -2.61646688e-01
-4.48808700e-01 -1.01600051e+00 -4.60342914e-01 -5.80443919e-01
-3.89622748e-01 6.52243853e-01 2.17405543e-01 -8.05316865... | [14.103271484375, 13.317792892456055] |
2a761574-b535-4028-b16a-6c6f177fa5a5 | active-learning-to-classify-macromolecular | 2102.1204 | null | https://arxiv.org/abs/2102.12040v2 | https://arxiv.org/pdf/2102.12040v2.pdf | Active Learning to Classify Macromolecular Structures in situ for Less Supervision in Cryo-Electron Tomography | Motivation: Cryo-Electron Tomography (cryo-ET) is a 3D bioimaging tool that visualizes the structural and spatial organization of macromolecules at a near-native state in single cells, which has broad applications in life science. However, the systematic structural recognition and recovery of macromolecules captured by... | ['Min Xu', 'Jing Zhang', 'Yi-Wei Chang', 'Xiangrui Zeng', 'Zhenxi Zhu', 'Haohan Wang', 'Xuefeng Du'] | 2021-02-24 | null | null | null | null | ['electron-tomography'] | ['medical'] | [ 1.20635070e-01 5.71141504e-02 -2.95678616e-01 -4.45726782e-01
-1.02367067e+00 -6.18627787e-01 2.57156044e-01 3.86810005e-01
-4.02753115e-01 1.07856607e+00 -2.70924211e-01 -2.58499265e-01
-2.21878923e-02 -7.58699000e-01 -7.65657723e-01 -1.30217731e+00
2.04534501e-01 9.52685952e-01 3.27295899e-01 3.94908100... | [13.683465003967285, -3.0727052688598633] |
9a1e8f74-ec58-4653-9784-38fbaf1e74cf | why-having-10000-parameters-in-your-camera | 1912.02908 | null | https://arxiv.org/abs/1912.02908v3 | https://arxiv.org/pdf/1912.02908v3.pdf | Why Having 10,000 Parameters in Your Camera Model is Better Than Twelve | Camera calibration is an essential first step in setting up 3D Computer Vision systems. Commonly used parametric camera models are limited to a few degrees of freedom and thus often do not optimally fit to complex real lens distortion. In contrast, generic camera models allow for very accurate calibration due to their ... | ['Thomas Schöps', 'Viktor Larsson', 'Marc Pollefeys', 'Torsten Sattler'] | 2019-12-05 | null | null | null | null | ['stereo-depth-estimation'] | ['computer-vision'] | [-1.38561383e-01 -2.88526993e-02 1.90560278e-02 -3.58105630e-01
-4.36213702e-01 -9.67857420e-01 5.06327569e-01 -2.06094012e-01
-3.39627236e-01 4.44922298e-01 8.08049738e-02 -2.81472892e-01
1.33579627e-01 -4.02975440e-01 -8.55733633e-01 -3.16092640e-01
6.61846399e-01 7.26737022e-01 3.57279003e-01 6.36105612... | [8.220479011535645, -2.394458055496216] |
99bd4ba2-f62d-40ad-8b97-d89756812d63 | fine-tuning-of-sign-language-recognition | 2302.07693 | null | https://arxiv.org/abs/2302.07693v2 | https://arxiv.org/pdf/2302.07693v2.pdf | Fine-tuning of sign language recognition models: a technical report | Sign Language Recognition (SLR) is an essential yet challenging task since sign language is performed with the fast and complex movement of hand gestures, body posture, and even facial expressions. %Skeleton Aware Multi-modal Sign Language Recognition In this work, we focused on investigating two questions: how fine-tu... | ['Iuliia Zemtsova', 'Dmitriy Milevich', 'Ruslan Murtazin', 'Leonid Verkhovtsev', 'Maxim Novopoltsev'] | 2023-02-15 | null | null | null | null | ['sign-language-recognition', 'gesture-recognition'] | ['computer-vision', 'computer-vision'] | [-7.18333796e-02 -3.07808578e-01 -7.98999220e-02 -3.48796546e-01
-5.59384108e-01 -3.05298239e-01 4.36197817e-01 -1.10195470e+00
-8.10505033e-01 3.36154640e-01 3.37233365e-01 -2.84131140e-01
1.50204256e-01 -3.22392166e-01 -2.66506076e-01 -6.96039736e-01
3.17003429e-02 6.41702235e-01 5.23315728e-01 -2.77672172... | [9.120390892028809, -6.431921482086182] |
5d3edf72-9dce-46a9-9d49-6730a5f7a1d8 | amr-parsing-with-instruction-fine-tuned-pre | 2304.12272 | null | https://arxiv.org/abs/2304.12272v1 | https://arxiv.org/pdf/2304.12272v1.pdf | AMR Parsing with Instruction Fine-tuned Pre-trained Language Models | Instruction fine-tuned language models on a collection of instruction annotated datasets (FLAN) have shown highly effective to improve model performance and generalization to unseen tasks. However, a majority of standard parsing tasks including abstract meaning representation (AMR), universal dependency (UD), semantic ... | ['Salim Roukos', 'Tahira Naseem', 'Radu Florian', 'Ramón Fernandez Astudillo', 'Young-suk Lee'] | 2023-04-24 | null | null | null | null | ['amr-parsing', 'semantic-role-labeling'] | ['natural-language-processing', 'natural-language-processing'] | [ 8.67710561e-02 1.41001090e-01 -5.86592972e-01 -6.46496475e-01
-9.98076200e-01 -7.00098872e-01 3.84734601e-01 2.18382388e-01
-3.17630410e-01 6.90228403e-01 3.93453568e-01 -9.82446849e-01
3.50614935e-01 -7.03218460e-01 -8.82133245e-01 -3.98294516e-02
2.09494755e-01 1.99547485e-01 3.82391095e-01 -6.35674655... | [10.419587135314941, 8.67853832244873] |
e451b653-9007-4d05-8ae4-8fc2cc3e0515 | fe-fusion-vpr-attention-based-multi-scale | 2211.12244 | null | https://arxiv.org/abs/2211.12244v2 | https://arxiv.org/pdf/2211.12244v2.pdf | FE-Fusion-VPR: Attention-based Multi-Scale Network Architecture for Visual Place Recognition by Fusing Frames and Events | Traditional visual place recognition (VPR), usually using standard cameras, is easy to fail due to glare or high-speed motion. By contrast, event cameras have the advantages of low latency, high temporal resolution, and high dynamic range, which can deal with the above issues. Nevertheless, event cameras are prone to f... | ['Zheng Fang', 'XinJie Huang', 'Hao Zhuang', 'Junjie Jiang', 'Delei Kong', 'Kuanxu Hou'] | 2022-11-22 | null | null | null | null | ['visual-place-recognition'] | ['computer-vision'] | [-1.68745220e-01 -8.13002110e-01 -1.21364728e-01 -2.23269552e-01
-8.07361245e-01 -3.07547867e-01 6.38269544e-01 1.46270022e-01
-6.51589513e-01 5.07842541e-01 1.10882059e-01 2.61833161e-01
8.30622092e-02 -7.27687955e-01 -5.72487772e-01 -7.17055202e-01
3.77110355e-02 -2.07923383e-01 7.12711692e-01 -1.98898226... | [8.510790824890137, -1.1928642988204956] |
dbe6346d-99a1-4e8c-ab2b-4314098bdf20 | self-knowledge-distillation-in-natural | 1908.01851 | null | https://arxiv.org/abs/1908.01851v1 | https://arxiv.org/pdf/1908.01851v1.pdf | Self-Knowledge Distillation in Natural Language Processing | Since deep learning became a key player in natural language processing (NLP), many deep learning models have been showing remarkable performances in a variety of NLP tasks, and in some cases, they are even outperforming humans. Such high performance can be explained by efficient knowledge representation of deep learnin... | ['Heeyoul Choi', 'Sangchul Hahn'] | 2019-08-02 | self-knowledge-distillation-in-natural-1 | https://aclanthology.org/R19-1050 | https://aclanthology.org/R19-1050.pdf | ranlp-2019-9 | ['self-knowledge-distillation'] | ['computer-vision'] | [ 6.65315986e-02 3.06434184e-01 -5.56261957e-01 -3.94002408e-01
-4.59623516e-01 -3.94185573e-01 8.18340838e-01 5.53843156e-02
-7.25239098e-01 8.88822377e-01 3.99860263e-01 -2.45245010e-01
1.11487828e-01 -8.37544084e-01 -7.01054931e-01 -6.57287300e-01
3.14356387e-01 5.80267251e-01 9.14950445e-02 -8.16695839... | [10.541481018066406, 8.791991233825684] |
41ede22c-6f0d-4e16-a656-cedabe163b76 | diffeomorphic-temporal-alignment-nets | null | null | https://neurips.cc/Conferences/2019/Schedule?showEvent=13767 | https://www.cs.bgu.ac.il/~orenfr/DTAN/ShapiraWeber_NeurIPS_2019.pdf | Diffeomorphic Temporal Alignment Nets | Time-series analysis is confounded by nonlinear time warping of the data. Traditional methods for joint alignment do not generalize: after aligning a given signal ensemble, they lack a mechanism, that does not require solving a new optimization problem, to align previously-unseen signals. In the multi-class case, they ... | ['Oren Shriki', 'Ron Shapira Weber', 'Nicki Skafte', 'Matan Eyal', 'Oren Freifeld'] | 2019-12-10 | diffeomorphic-temporal-alignment-nets-1 | http://papers.nips.cc/paper/8884-diffeomorphic-temporal-alignment-nets | http://papers.nips.cc/paper/8884-diffeomorphic-temporal-alignment-nets.pdf | neurips-2019-12 | ['ecg-classification', 'electrocardiography-ecg', 'time-series-alignment', 'time-series-averaging'] | ['medical', 'methodology', 'time-series', 'time-series'] | [ 1.91194758e-01 -8.50397050e-02 -1.47999153e-01 -4.48333323e-01
-9.85209167e-01 -9.51492906e-01 6.71559155e-01 -3.83345276e-01
-3.51248354e-01 6.25357151e-01 1.07803911e-01 -1.45735577e-01
-2.20827535e-01 -3.74368578e-01 -6.37688816e-01 -9.08206224e-01
-2.35909417e-01 7.14521229e-01 1.32027939e-02 -1.19644716... | [7.4415178298950195, 3.18198823928833] |
d5344734-958e-4816-b221-55b73580ccfd | large-scale-product-categorization-using | 1903.04254 | null | http://arxiv.org/abs/1903.04254v1 | http://arxiv.org/pdf/1903.04254v1.pdf | Large Scale Product Categorization using Structured and Unstructured Attributes | Product categorization using text data for eCommerce is a very challenging
extreme classification problem with several thousands of classes and several
millions of products to classify. Even though multi-class text classification
is a well studied problem both in academia and industry, most approaches either
deal with ... | ['Abilash Amarthaluri', 'Abhinandan Krishnan'] | 2019-03-01 | null | null | null | null | ['product-categorization'] | ['miscellaneous'] | [ 1.52492560e-02 -3.29871267e-01 -2.03704178e-01 -7.61432886e-01
-1.42459065e-01 -1.04134667e+00 5.20467401e-01 8.25848460e-01
-2.05007941e-01 3.92658919e-01 7.74445310e-02 -2.71195799e-01
-4.51604962e-01 -1.04467738e+00 -5.00980973e-01 -4.90409821e-01
2.89324299e-02 9.12079334e-01 -7.27897882e-02 -4.15602952... | [9.920587539672852, 6.185657978057861] |
1f19e8d4-04f1-4029-b9fa-e26781dd044a | knowledge-augmented-graph-neural-networks | 2301.10451 | null | https://arxiv.org/abs/2301.10451v1 | https://arxiv.org/pdf/2301.10451v1.pdf | Knowledge-augmented Graph Neural Networks with Concept-aware Attention for Adverse Drug Event Detection | Adverse drug events (ADEs) are an important aspect of drug safety. Various texts such as biomedical literature, drug reviews, and user posts on social media and medical forums contain a wealth of information about ADEs. Recent studies have applied word embedding and deep learning -based natural language processing to a... | ['Pekka Marttinen', 'Ya Gao', 'Shaoxiong Ji'] | 2023-01-25 | null | null | null | null | ['relational-reasoning'] | ['natural-language-processing'] | [-1.22487791e-01 3.17258574e-02 -7.55902648e-01 -1.40104562e-01
-5.25319934e-01 -3.19819331e-01 5.78454792e-01 1.07707870e+00
-5.37333250e-01 5.62120259e-01 8.23402405e-01 -5.54302633e-01
-2.39869639e-01 -1.12798214e+00 -4.14326012e-01 -3.09970737e-01
-3.44907403e-01 3.78968060e-01 -5.87315373e-02 -2.28440106... | [7.872866630554199, 7.00676155090332] |
656f4eef-86c0-4c80-b07d-6b2ea782b81f | if-only-we-had-better-counterfactual | 2103.01035 | null | https://arxiv.org/abs/2103.01035v1 | https://arxiv.org/pdf/2103.01035v1.pdf | If Only We Had Better Counterfactual Explanations: Five Key Deficits to Rectify in the Evaluation of Counterfactual XAI Techniques | In recent years, there has been an explosion of AI research on counterfactual explanations as a solution to the problem of eXplainable AI (XAI). These explanations seem to offer technical, psychological and legal benefits over other explanation techniques. We survey 100 distinct counterfactual explanation methods repor... | ['Barry Smyth', 'Eoin Delaney', 'Eoin M Kenny', 'Mark T Keane'] | 2021-02-26 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [ 4.71406162e-01 1.05658889e+00 -7.10643828e-01 -2.95020580e-01
-2.83264339e-01 -5.62540233e-01 1.03807402e+00 7.76166692e-02
-2.59726137e-01 1.21656215e+00 6.60158038e-01 -1.07488048e+00
-6.59102857e-01 -2.18435064e-01 -4.77089435e-01 -1.71988770e-01
-8.26195907e-03 6.30389988e-01 -4.59114492e-01 -9.53490958... | [8.671022415161133, 5.751046180725098] |
8a3a6f0e-ddd4-4895-9c75-d833d27ce491 | upi-net-semantic-contour-detection-in | 1909.00229 | null | https://arxiv.org/abs/1909.00229v2 | https://arxiv.org/pdf/1909.00229v2.pdf | UPI-Net: Semantic Contour Detection in Placental Ultrasound | Semantic contour detection is a challenging problem that is often met in medical imaging, of which placental image analysis is a particular example. In this paper, we investigate utero-placental interface (UPI) detection in 2D placental ultrasound images by formulating it as a semantic contour detection problem. As opp... | ['Sally Collins', 'J. Alison Noble', 'Huan Qi'] | 2019-08-31 | null | null | null | null | ['contour-detection'] | ['computer-vision'] | [ 9.07685101e-01 6.54991090e-01 1.93645120e-01 -5.95797598e-01
-9.31631267e-01 -5.29545426e-01 4.79799479e-01 6.24348760e-01
7.90014490e-02 2.33863890e-01 3.96920554e-02 -4.21431214e-01
-2.50226021e-01 -7.66550899e-01 -7.11924732e-01 -5.18966496e-01
-6.96055114e-01 6.64945483e-01 7.63616204e-01 1.84248403... | [14.465187072753906, -2.5313587188720703] |
9ffff2ca-3664-4a59-89e0-df7f806779d0 | instance-dependent-partial-label-learning | 2110.12911 | null | https://arxiv.org/abs/2110.12911v2 | https://arxiv.org/pdf/2110.12911v2.pdf | Instance-Dependent Partial Label Learning | Partial label learning (PLL) is a typical weakly supervised learning problem, where each training example is associated with a set of candidate labels among which only one is true. Most existing PLL approaches assume that the incorrect labels in each training example are randomly picked as the candidate labels. However... | ['Min-Ling Zhang', 'Xin Geng', 'Congyu Qiao', 'Ning Xu'] | 2021-10-25 | null | http://proceedings.neurips.cc/paper/2021/hash/e38e37a99f7de1f45d169efcdb288dd1-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/e38e37a99f7de1f45d169efcdb288dd1-Paper.pdf | neurips-2021-12 | ['partial-label-learning'] | ['methodology'] | [ 3.07632148e-01 4.24453437e-01 -9.51659918e-01 -6.34367526e-01
-8.46538365e-01 -4.06594992e-01 4.76560891e-01 2.71098316e-01
-2.13295951e-01 8.87784421e-01 -1.72276080e-01 1.02589093e-01
2.37878338e-02 -7.55035996e-01 -7.85520017e-01 -1.07703114e+00
5.83488047e-01 7.67651618e-01 8.13283473e-02 6.14567280... | [9.442756652832031, 4.040003299713135] |
87605565-0628-42e6-95ff-8cf30b362ced | panda-llm-training-data-and-evaluation-for | 2305.03025 | null | https://arxiv.org/abs/2305.03025v1 | https://arxiv.org/pdf/2305.03025v1.pdf | Panda LLM: Training Data and Evaluation for Open-Sourced Chinese Instruction-Following Large Language Models | This project focuses on enhancing open-source large language models through instruction-tuning and providing comprehensive evaluations of their performance. We explore how various training data factors, such as quantity, quality, and linguistic distribution, influence the performance of instruction-tuned models trained... | ['Zhanfeng Mo', 'Tianze Luo', 'Bosheng Ding', 'Fangkai Jiao'] | 2023-05-04 | null | null | null | null | ['instruction-following'] | ['natural-language-processing'] | [-6.05144799e-01 -2.48756036e-01 -7.62452304e-01 -5.43478727e-01
-1.06709397e+00 -7.07613468e-01 2.70975351e-01 2.16660142e-01
-6.54236972e-01 8.25045228e-01 5.16911149e-01 -8.02996874e-01
5.51553726e-01 -5.37679315e-01 -7.02826500e-01 4.68970202e-02
9.73620787e-02 1.64012805e-01 1.88440621e-01 -5.26553214... | [10.761756896972656, 8.342376708984375] |
e43603fd-bb62-41b7-94d7-fe3ea5895be5 | type-to-track-retrieve-any-object-via-prompt | 2305.13495 | null | https://arxiv.org/abs/2305.13495v1 | https://arxiv.org/pdf/2305.13495v1.pdf | Type-to-Track: Retrieve Any Object via Prompt-based Tracking | One of the recent trends in vision problems is to use natural language captions to describe the objects of interest. This approach can overcome some limitations of traditional methods that rely on bounding boxes or category annotations. This paper introduces a novel paradigm for Multiple Object Tracking called Type-to-... | ['Khoa Luu', 'Kris Kitani', 'Kha Gia Quach', 'Pha Nguyen'] | 2023-05-22 | null | null | null | null | ['multiple-object-tracking'] | ['computer-vision'] | [ 7.17059076e-02 -4.71636027e-01 -2.97832251e-01 -4.32702214e-01
-8.20513844e-01 -6.99197829e-01 5.16953111e-01 1.12339243e-01
-3.47687811e-01 3.86167914e-01 6.33992031e-02 5.55656664e-02
1.58964649e-01 -2.46130943e-01 -8.68086755e-01 -5.23363113e-01
-8.55292156e-02 3.80446941e-01 7.98759282e-01 9.81214717... | [6.432694435119629, -1.9905036687850952] |
b3104e85-9d31-481a-97c8-3e4707cd69e9 | authorship-attribution-using-text-distortion | null | null | https://aclanthology.org/E17-1107 | https://aclanthology.org/E17-1107.pdf | Authorship Attribution Using Text Distortion | Authorship attribution is associated with important applications in forensics and humanities research. A crucial point in this field is to quantify the personal style of writing, ideally in a way that is not affected by changes in topic or genre. In this paper, we present a novel method that enhances authorship attribu... | ['Efstathios Stamatatos'] | 2017-04-01 | null | null | null | eacl-2017-4 | ['authorship-verification'] | ['natural-language-processing'] | [ 1.39636740e-01 -1.46012217e-01 -1.46517679e-01 -1.45925850e-01
-3.07935774e-01 -8.28416646e-01 7.85753787e-01 4.62786436e-01
-5.62467754e-01 7.62456536e-01 1.77630916e-01 -7.59812221e-02
-1.82882801e-01 -3.90217632e-01 -1.76267758e-01 -3.42048585e-01
5.22115469e-01 4.92876023e-01 8.79463404e-02 1.64163515... | [9.58076000213623, 10.61246395111084] |
e5bdeb74-8ef9-4e6b-9513-0558186a2a3c | multi-view-convolutional-neural-networks-for | 1505.0088 | null | http://arxiv.org/abs/1505.00880v3 | http://arxiv.org/pdf/1505.00880v3.pdf | Multi-view Convolutional Neural Networks for 3D Shape Recognition | A longstanding question in computer vision concerns the representation of 3D
shapes for recognition: should 3D shapes be represented with descriptors
operating on their native 3D formats, such as voxel grid or polygon mesh, or
can they be effectively represented with view-based descriptors? We address
this question in ... | ['Erik Learned-Miller', 'Subhransu Maji', 'Hang Su', 'Evangelos Kalogerakis'] | 2015-05-05 | multi-view-convolutional-neural-networks-for-1 | http://openaccess.thecvf.com/content_iccv_2015/html/Su_Multi-View_Convolutional_Neural_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Su_Multi-View_Convolutional_Neural_ICCV_2015_paper.pdf | iccv-2015-12 | ['3d-shape-recognition'] | ['computer-vision'] | [ 1.98134892e-02 -1.13876797e-01 1.13311917e-01 -6.28444612e-01
-5.20658731e-01 -1.02755928e+00 8.79363239e-01 -3.84352840e-02
9.70781744e-02 -2.63770670e-01 1.99605301e-01 -1.85811058e-01
-1.66072138e-03 -9.49665129e-01 -5.29596746e-01 -2.98882186e-01
5.44451832e-05 8.06080043e-01 1.99926242e-01 -2.71068066... | [8.205255508422852, -3.7848899364471436] |
c1f59a42-5695-436b-b338-b7941a2c27cf | exploring-the-boundaries-of-semi-supervised | 2306.01229 | null | https://arxiv.org/abs/2306.01229v1 | https://arxiv.org/pdf/2306.01229v1.pdf | Exploring the Boundaries of Semi-Supervised Facial Expression Recognition: Learning from In-Distribution, Out-of-Distribution, and Unconstrained Data | Deep learning-based methods have been the key driving force behind much of the recent success of facial expression recognition (FER) systems. However, the need for large amounts of labelled data remains a challenge. Semi-supervised learning offers a way to overcome this limitation, allowing models to learn from a small... | ['Ali Etemad', 'Shuvendu Roy'] | 2023-06-02 | null | null | null | null | ['facial-expression-recognition', 'pseudo-label'] | ['computer-vision', 'miscellaneous'] | [ 3.11345667e-01 1.75698042e-01 -3.36306274e-01 -7.86174715e-01
-7.57717133e-01 -2.61357218e-01 6.73008919e-01 -3.53745997e-01
-4.32673573e-01 8.48723650e-01 -1.16929531e-01 -4.46148366e-02
-1.87461644e-01 -2.97479242e-01 -4.79207367e-01 -9.16624784e-01
-2.91354302e-02 6.34882748e-01 -2.02759907e-01 -1.40378997... | [13.565072059631348, 1.6102700233459473] |
fa4d015b-9ae3-494c-a191-387e03556c16 | hard-regularization-to-prevent-collapse-in | 2303.16521 | null | https://arxiv.org/abs/2303.16521v1 | https://arxiv.org/pdf/2303.16521v1.pdf | Hard Regularization to Prevent Collapse in Online Deep Clustering without Data Augmentation | Online deep clustering refers to the joint use of a feature extraction network and a clustering model to assign cluster labels to each new data point or batch as it is processed. While faster and more versatile than offline methods, online clustering can easily reach the collapsed solution where the encoder maps all in... | ['Thomas Lukasiewicz', 'Louis Mahon'] | 2023-03-29 | null | null | null | null | ['online-clustering', 'deep-clustering', 'deep-clustering'] | ['computer-vision', 'miscellaneous', 'natural-language-processing'] | [-1.08124450e-01 1.66147575e-01 -1.58580381e-03 -7.21021593e-01
-6.45089984e-01 -7.78531551e-01 6.30460680e-01 3.19892794e-01
-5.45774817e-01 1.97603345e-01 -4.00716476e-02 -2.19885319e-01
-3.77421170e-01 -4.76315737e-01 -8.62223685e-01 -8.14121008e-01
-1.09672643e-01 8.95934463e-01 1.11328810e-01 4.60495442... | [9.109963417053223, 3.2882373332977295] |
54ab371e-0e69-4246-bfc6-2f85227c8a39 | new-constraints-on-radiative-seesaw-models | 2103.06881 | null | https://arxiv.org/abs/2103.06881v1 | https://arxiv.org/pdf/2103.06881v1.pdf | New constraints on radiative seesaw models from IceCube and other neutrino detectors | Dark matter (DM) scattering and its subsequent capture in the Sun can boost the local relic density, leading to an enhanced neutrino flux from DM annihilations that is in principle detectable at neutrino telescopes. We calculate the event rates expected for a radiative seesaw model containing both scalar triplet and si... | ['S. Zeinstra', 'M. Klasen', 'A. Kappes', 'R. Busse', 'T. de Boer'] | 2021-03-11 | null | null | null | null | ['pico'] | ['natural-language-processing'] | [ 8.35428089e-02 2.41590336e-01 -1.26645535e-01 4.35291566e-02
1.76894724e-01 -4.35619414e-01 1.37239182e+00 -4.38644320e-01
-5.71285367e-01 1.20500696e+00 -6.74774870e-02 -6.79075241e-01
5.33795595e-01 -1.04973471e+00 -2.64091402e-01 -1.32076466e+00
3.49751323e-01 9.32311773e-01 7.26219773e-01 -2.51332909... | [7.081794738769531, 3.401240348815918] |
30381288-818a-467b-8187-53194e26b71b | improved-code-summarization-via-a-graph | 2004.02843 | null | https://arxiv.org/abs/2004.02843v2 | https://arxiv.org/pdf/2004.02843v2.pdf | Improved Code Summarization via a Graph Neural Network | Automatic source code summarization is the task of generating natural language descriptions for source code. Automatic code summarization is a rapidly expanding research area, especially as the community has taken greater advantage of advances in neural network and AI technologies. In general, source code summarization... | ['Sakib Haque', 'Lingfei Wu', 'Alexander LeClair', 'Collin McMillan'] | 2020-04-06 | null | null | null | null | ['code-summarization'] | ['computer-code'] | [ 3.59208375e-01 5.79808652e-01 -3.19193274e-01 -3.25439602e-01
-8.85102987e-01 -5.21814585e-01 3.34125608e-01 8.30414653e-01
7.69200623e-02 3.87393594e-01 6.57646060e-01 -4.86319959e-01
3.38626236e-01 -5.95252275e-01 -5.89496017e-01 -1.19426459e-01
-6.57971352e-02 2.54316092e-01 3.65608513e-01 -3.22309613... | [7.635158061981201, 7.924810886383057] |
4429ccf3-8657-4a5e-bd50-235d8e902f87 | a-framework-for-the-automated | 2303.08858 | null | https://arxiv.org/abs/2303.08858v1 | https://arxiv.org/pdf/2303.08858v1.pdf | A Framework for the Automated Parameterization of a Sensorless Bearing Fault Detection Pipeline | This study proposes a framework for the automated hyperparameter optimization of a bearing fault detection pipeline for permanent magnet synchronous motors (PMSMs) without the need of external sensors. A automated machine learning (AutoML) pipeline search is performed by means of a genetic optimization to reduce human ... | ['Elmar Engels', 'Alexander Gepperth', 'Tobias Wagner'] | 2023-03-15 | null | null | null | null | ['automl', 'fault-detection'] | ['methodology', 'miscellaneous'] | [ 3.96439195e-01 1.62836194e-01 2.67662823e-01 8.63098577e-02
-1.36409670e-01 -3.09837043e-01 5.56083858e-01 2.37900466e-01
-3.13716143e-01 3.78657967e-01 -7.20138311e-01 -2.54722595e-01
-9.88687098e-01 -3.92163008e-01 -4.71415877e-01 -8.86612713e-01
3.93536054e-02 8.16813827e-01 7.97044411e-02 -2.54894942... | [6.696925163269043, 2.3996684551239014] |
d4865297-51f5-4eb9-910f-8f718b51ae7f | on-the-security-vulnerabilities-of-text-to | 2211.15363 | null | https://arxiv.org/abs/2211.15363v2 | https://arxiv.org/pdf/2211.15363v2.pdf | On the Security Vulnerabilities of Text-to-SQL Models | Although it has been demonstrated that Natural Language Processing (NLP) algorithms are vulnerable to deliberate attacks, the question of whether such weaknesses can lead to software security threats is under-explored. To bridge this gap, we conducted vulnerability tests on Text-to-SQL systems that are commonly used to... | ['Mark Stevenson', 'Jingfeng Yang', 'YiPeng Zhang', 'Xutan Peng'] | 2022-11-28 | null | null | null | null | ['text-to-sql'] | ['computer-code'] | [ 1.80458531e-01 1.95390046e-01 -2.28662044e-01 -2.21081570e-01
-7.91958630e-01 -1.29429364e+00 5.86413145e-01 5.55706203e-01
-4.41638194e-03 9.68258157e-02 -1.28922611e-01 -1.37615609e+00
3.68694961e-01 -9.77701962e-01 -8.13189566e-01 1.10646427e-01
-1.95124716e-01 -1.58857480e-01 5.60146272e-01 -9.77189913... | [6.190718173980713, 7.817381381988525] |
95aedf75-daff-4d1c-9d54-997efc9d1846 | promptbench-towards-evaluating-the-robustness | 2306.04528 | null | https://arxiv.org/abs/2306.04528v2 | https://arxiv.org/pdf/2306.04528v2.pdf | PromptBench: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts | The increasing reliance on Large Language Models (LLMs) across academia and industry necessitates a comprehensive understanding of their robustness to prompts. In response to this vital need, we introduce PromptBench, a robustness benchmark designed to measure LLMs' resilience to adversarial prompts. This study uses a ... | ['Xing Xie', 'Yue Zhang', 'Neil Zhenqiang Gong', 'Wei Ye', 'Linyi Yang', 'Yidong Wang', 'Hao Chen', 'Zichen Wang', 'Jiaheng Zhou', 'Jindong Wang', 'Kaijie Zhu'] | 2023-06-07 | null | null | null | null | ['natural-language-inference', 'sentiment-analysis', 'reading-comprehension'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 3.19375962e-01 -2.06291184e-01 6.71163350e-02 -1.84277281e-01
-1.19591916e+00 -1.35102999e+00 7.33338594e-01 5.06023645e-01
-3.75698745e-01 5.47396719e-01 6.87579095e-01 -7.66590476e-01
7.33551010e-02 -5.63082874e-01 -7.25068748e-01 -9.36730877e-02
2.25981906e-01 -1.31715029e-01 -1.69680834e-01 -6.21689320... | [6.170124530792236, 8.156461715698242] |
1496d5e9-ff29-4fdc-a3d6-abc2c89946c2 | improving-the-quality-of-mt-output-using | 1310.0573 | null | http://arxiv.org/abs/1310.0573v1 | http://arxiv.org/pdf/1310.0573v1.pdf | Improving the Quality of MT Output using Novel Name Entity Translation Scheme | This paper presents a novel approach to machine translation by combining the
state of art name entity translation scheme. Improper translation of name
entities lapse the quality of machine translated output. In this work, name
entities are transliterated by using statistical rule based approach. This
paper describes th... | ['Nisheeth Joshi', 'Iti Mathur', 'Deepti Bhalla'] | 2013-10-02 | null | null | null | null | ['miscellaneous'] | ['miscellaneous'] | [-2.99681500e-02 -7.21743032e-02 -7.63760507e-02 -4.04729664e-01
-7.88554072e-01 -9.93917406e-01 7.36822963e-01 -9.35312286e-02
-4.75347131e-01 1.47603726e+00 4.88204598e-01 -8.44103456e-01
8.15146491e-02 -7.88686872e-01 -3.04950088e-01 -2.62601256e-01
5.80761135e-01 9.77060616e-01 -2.80429780e-01 -4.37667847... | [11.310731887817383, 10.486783027648926] |
b2321d3f-c81e-45fc-be7e-871d3938bc33 | realization-rgbd-image-stylization | 2305.06565 | null | https://arxiv.org/abs/2305.06565v1 | https://arxiv.org/pdf/2305.06565v1.pdf | Realization RGBD Image Stylization | This research paper explores the application of style transfer in computer vision using RGB images and their corresponding depth maps. We propose a novel method that incorporates the depth map and a heatmap of the RGB image to generate more realistic style transfer results. We compare our method to the traditional neur... | ['Aparna Mendu', 'Vaishnavi Mendu', 'Bhavya Sehgal'] | 2023-05-11 | null | null | null | null | ['style-transfer', 'image-stylization'] | ['computer-vision', 'computer-vision'] | [ 5.06558359e-01 -6.59283996e-02 4.75742221e-01 -5.26608586e-01
-1.53475374e-01 -6.76343381e-01 5.63873768e-01 -6.48245394e-01
-2.76110321e-01 7.35018909e-01 3.30667645e-02 -2.79871583e-01
4.40595448e-01 -1.15114164e+00 -6.91236138e-01 -3.29819500e-01
6.88565254e-01 -5.80177009e-02 2.87076503e-01 -4.18030322... | [10.0615234375, -2.4155783653259277] |
d6c2aea6-f913-4000-aedd-37ffa65037d0 | efficient-neural-network-compression-via | null | null | https://openreview.net/forum?id=S1g5uBnusm | https://openreview.net/pdf?id=S1g5uBnusm | Efficient Neural Network Compression via Transfer Learning for Industrial Optical Inspection | In this paper, we investigate learning the deep neural networks for automated optical inspection in industrial manufacturing. Our preliminary result has shown the stunning performance improvement by transfer learning from the completely dissimilar source domain: ImageNet. Further study for demystifying this improvement... | ['Frank C. Park', 'Yung-Kyun Noh', 'Seunghyeon Kim'] | 2018-10-20 | null | null | null | null | ['neural-network-compression', 'neural-network-compression'] | ['methodology', 'miscellaneous'] | [ 2.24903777e-01 5.31261206e-01 5.91793954e-01 -4.22029823e-01
-1.70941055e-01 -1.07780099e-01 1.78615630e-01 -1.68364853e-01
-5.95957756e-01 8.71203542e-01 -2.82197297e-01 -4.57668215e-01
-4.36430454e-01 -8.79880190e-01 -1.04804623e+00 -8.58193099e-01
-1.73838854e-01 4.54748064e-01 8.84744152e-02 -3.40552539... | [8.002182960510254, 2.330394983291626] |
9e9b01da-b8dd-4995-980f-882f3d16ccaf | increasing-behavioral-complexity-for-evolved | 1510.07957 | null | http://arxiv.org/abs/1510.07957v1 | http://arxiv.org/pdf/1510.07957v1.pdf | Increasing Behavioral Complexity for Evolved Virtual Creatures with the ESP Method | Since their introduction in 1994 (Sims), evolved virtual creatures (EVCs)
have employed the coevolution of morphology and control to produce high-impact
work in multiple fields, including graphics, evolutionary computation,
robotics, and artificial life. However, in contrast to fixed-morphology
creatures, there has bee... | ['Sebastian Risi', 'Risto Miikkulainen', 'Dan Lessin', 'Don Fussell'] | 2015-10-27 | null | null | null | null | ['artificial-life'] | ['miscellaneous'] | [ 5.63470833e-02 1.13735430e-01 5.91156125e-01 1.50809869e-01
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-2.59487271e-01 2.53119737e-01 5.35545170e-01 -6.16894722... | [5.653899192810059, 3.901423215866089] |
d923fb20-5be6-4a52-9e0c-41e936107259 | resolving-language-and-vision-ambiguities-1 | null | null | https://aclanthology.org/D16-1156 | https://aclanthology.org/D16-1156.pdf | Resolving Language and Vision Ambiguities Together: Joint Segmentation \& Prepositional Attachment Resolution in Captioned Scenes | null | ['Kevin Kochersberger', 'Aishwarya Agrawal', 'Yash Goyal', 'Stanislaw Antol', 'Dhruv Batra', 'Ankit Laddha', 'Gordon Christie'] | 2016-11-01 | null | null | null | emnlp-2016-11 | ['prepositional-phrase-attachment'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
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-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.3825860023498535, 3.6900131702423096] |
e592411a-ac4c-4715-9f81-782e8c4cf975 | deep-implicit-distribution-alignment-networks | 2302.08921 | null | https://arxiv.org/abs/2302.08921v1 | https://arxiv.org/pdf/2302.08921v1.pdf | Deep Implicit Distribution Alignment Networks for Cross-Corpus Speech Emotion Recognition | In this paper, we propose a novel deep transfer learning method called deep implicit distribution alignment networks (DIDAN) to deal with cross-corpus speech emotion recognition (SER) problem, in which the labeled training (source) and unlabeled testing (target) speech signals come from different corpora. Specifically,... | ['Li Zhao', 'Hailun Lian', 'Wenming Zheng', 'Yuan Zong', 'Jincen Wang', 'Yan Zhao'] | 2023-02-17 | null | null | null | null | ['cross-corpus', 'speech-emotion-recognition'] | ['computer-vision', 'speech'] | [ 4.40467708e-02 -9.37353373e-02 1.78493083e-01 -7.23915219e-01
-1.00101447e+00 -1.73379213e-01 3.83832693e-01 -3.29987735e-01
-3.88711363e-01 5.99938095e-01 -8.26558918e-02 -1.08222686e-01
2.48994470e-01 -2.10423425e-01 -5.33967435e-01 -9.46052372e-01
7.01232776e-02 2.17742994e-01 -2.74015307e-01 -3.36517036... | [13.932955741882324, 6.006892681121826] |
5897e751-9107-491e-a1f9-2287be0c1ecc | knowledge-informed-machine-learning-using-a | 2201.01769 | null | https://arxiv.org/abs/2201.01769v1 | https://arxiv.org/pdf/2201.01769v1.pdf | Knowledge Informed Machine Learning using a Weibull-based Loss Function | Machine learning can be enhanced through the integration of external knowledge. This method, called knowledge informed machine learning, is also applicable within the field of Prognostics and Health Management (PHM). In this paper, the various methods of knowledge informed machine learning, from a PHM context, are revi... | ['Chris K Mechefske', 'Tim von Hahn'] | 2022-01-04 | null | null | null | null | ['time-series-regression', 'remaining-useful-lifetime-estimation'] | ['time-series', 'time-series'] | [ 1.23752020e-01 3.52875233e-01 -2.98347533e-01 -5.17097414e-01
-7.06756294e-01 1.96696535e-01 1.54714808e-01 5.41983426e-01
-2.76570380e-01 1.27689767e+00 -2.06568792e-01 -4.26894486e-01
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-3.20809275e-01 3.94425809e-01 -2.06433415e-01 -1.45515174... | [6.614250659942627, 2.7787792682647705] |
e351002c-5318-42c0-84ed-8798ff6afe10 | intention-based-lane-changing-and-lane | 2011.07424 | null | https://arxiv.org/abs/2011.07424v1 | https://arxiv.org/pdf/2011.07424v1.pdf | Intention-Based Lane Changing and Lane Keeping Haptic Guidance Steering System | Haptic guidance in a shared steering assistance system has drawn significant attention in intelligent vehicle fields, owing to its mutual communication ability for vehicle control. By exerting continuous torque on the steering wheel, both the driver and support system can share lateral control of the vehicle. However, ... | ['Kimihiko Nakano', 'Tsutomu Kaizuka', 'Bo Yang', 'Zheng Wang', 'Kaiming Yang', 'Zhanhong Yan'] | 2020-11-15 | null | null | null | null | ['steering-control'] | ['computer-vision'] | [-2.29389265e-01 4.95071113e-01 -1.21618554e-01 -5.74840426e-01
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-4.45100814e-01 4.11473900e-01 -1.69113725e-02 -8.06553960e-01
-5.96794784e-02 -5.78182995e-01 -3.45903933e-01 -5.95872700e-01
6.19188026e-02 -1.85664326e-01 7.19200611e-01 -8.15064907... | [5.717960834503174, 1.0690232515335083] |
ba20a2b6-6876-4e0c-861f-4bb803514fca | weakly-and-semi-supervised-human-body-part | 1805.0431 | null | http://arxiv.org/abs/1805.04310v1 | http://arxiv.org/pdf/1805.04310v1.pdf | Weakly and Semi Supervised Human Body Part Parsing via Pose-Guided Knowledge Transfer | Human body part parsing, or human semantic part segmentation, is fundamental
to many computer vision tasks. In conventional semantic segmentation methods,
the ground truth segmentations are provided, and fully convolutional networks
(FCN) are trained in an end-to-end scheme. Although these methods have
demonstrated imp... | ['Yu-Wing Tai', 'Jianwen Xie', 'Guansong Lu', 'Hao-Shu Fang', 'Xiaolin Fang', 'Cewu Lu'] | 2018-05-11 | weakly-and-semi-supervised-human-body-part-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Fang_Weakly_and_Semi_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Fang_Weakly_and_Semi_CVPR_2018_paper.pdf | cvpr-2018-6 | ['human-part-segmentation', 'human-parsing'] | ['computer-vision', 'computer-vision'] | [ 3.24907780e-01 6.30414903e-01 -2.42223531e-01 -6.43213749e-01
-1.03563046e+00 -5.31269431e-01 3.21477234e-01 -2.36020871e-02
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3.42880636e-01 9.00406420e-01 6.71558559e-01 -2.01810062... | [8.304408073425293, -0.17778128385543823] |
12351321-ddc1-406c-a77d-f356596c7198 | generating-visual-spatial-description-via | 2305.11768 | null | https://arxiv.org/abs/2305.11768v2 | https://arxiv.org/pdf/2305.11768v2.pdf | Generating Visual Spatial Description via Holistic 3D Scene Understanding | Visual spatial description (VSD) aims to generate texts that describe the spatial relations of the given objects within images. Existing VSD work merely models the 2D geometrical vision features, thus inevitably falling prey to the problem of skewed spatial understanding of target objects. In this work, we investigate ... | ['Tat-Seng Chua', 'Min Zhang', 'Meishan Zhang', 'Jianguo Wei', 'Wei Ji', 'Hao Fei', 'Yu Zhao'] | 2023-05-19 | null | null | null | null | ['scene-understanding'] | ['computer-vision'] | [-7.39437388e-03 2.52600629e-02 -2.02844739e-02 -3.11359286e-01
-3.60141635e-01 -6.66975319e-01 8.25931489e-01 -8.34696442e-02
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-1.75136387e-01 -1.02284575e+00 -5.73638380e-01 -5.25520802e-01
3.92215997e-01 3.34143221e-01 4.03275430e-01 -3.06262732... | [10.571770668029785, 1.1479220390319824] |
8fb89175-ca46-45e3-a2a6-24cbeba49ac8 | elasticvit-conflict-aware-supernet-training | 2303.0973 | null | https://arxiv.org/abs/2303.09730v2 | https://arxiv.org/pdf/2303.09730v2.pdf | ElasticViT: Conflict-aware Supernet Training for Deploying Fast Vision Transformer on Diverse Mobile Devices | Neural Architecture Search (NAS) has shown promising performance in the automatic design of vision transformers (ViT) exceeding 1G FLOPs. However, designing lightweight and low-latency ViT models for diverse mobile devices remains a big challenge. In this work, we propose ElasticViT, a two-stage NAS approach that train... | ['Mao Yang', 'Zhi Wang', 'Yuqing Yang', 'Quanlu Zhang', 'Ting Cao', 'Jiahang Xu', 'Huiqiang Jiang', 'Li Lyna Zhang', 'Chen Tang'] | 2023-03-17 | null | null | null | null | ['architecture-search'] | ['methodology'] | [-1.58673510e-01 -3.11375111e-01 -5.08007169e-01 -1.89022124e-01
-3.43256533e-01 -5.13159990e-01 5.45383096e-02 -6.53482497e-01
-7.35930622e-01 5.60253978e-01 -3.40554208e-01 -6.02714777e-01
-1.76422894e-01 -6.44696176e-01 -8.21700871e-01 -4.81191099e-01
3.05755645e-01 5.98237574e-01 7.84825981e-01 5.47192581... | [8.588897705078125, 2.952136278152466] |
eee5437e-0750-4668-bab8-83470898b604 | evaluating-the-performance-of-stylegan2-ada | 2210.03786 | null | https://arxiv.org/abs/2210.03786v1 | https://arxiv.org/pdf/2210.03786v1.pdf | Evaluating the Performance of StyleGAN2-ADA on Medical Images | Although generative adversarial networks (GANs) have shown promise in medical imaging, they have four main limitations that impeded their utility: computational cost, data requirements, reliable evaluation measures, and training complexity. Our work investigates each of these obstacles in a novel application of StyleGA... | ['Kristy K. Brock', 'Ankit B. Patel', 'Yuan-Mao Lin', 'Brian De', 'Sireesha Yedururi', 'Aradhana M. Venkatesan', 'Hyunseon Christine Kang', 'Caroline Chung', 'Bruno Odisio', 'Eugene Koay', 'Ethan Lin', 'Suprateek Kundu', 'Brian M. Anderson', 'John Wood', 'McKell Woodland'] | 2022-10-07 | null | null | null | null | ['medical-image-generation'] | ['medical'] | [ 5.24378002e-01 2.89071649e-01 8.18043202e-02 -2.86300957e-01
-1.39920259e+00 -8.33848596e-01 3.06609660e-01 -4.58543569e-01
-5.57108879e-01 7.55567729e-01 6.95103332e-02 -6.84569240e-01
6.59559220e-02 -5.06196797e-01 -6.24443769e-01 -8.44690919e-01
-4.05534506e-01 4.68471676e-01 -3.04034531e-01 1.44316390... | [14.26960277557373, -1.9345295429229736] |
21b65332-1fc6-4086-9e2d-949bf9b8c70e | graph-neural-network-on-electronic-health | 1912.03761 | null | https://arxiv.org/abs/1912.03761v2 | https://arxiv.org/pdf/1912.03761v2.pdf | Variationally Regularized Graph-based Representation Learning for Electronic Health Records | Electronic Health Records (EHR) are high-dimensional data with implicit connections among thousands of medical concepts. These connections, for instance, the co-occurrence of diseases and lab-disease correlations can be informative when only a subset of these variables is documented by the clinician. A feasible approac... | ['Narges Razavian', 'Weicheng Zhu'] | 2019-12-08 | null | null | null | null | ['graph-structure-learning'] | ['graphs'] | [ 1.62644520e-01 6.68472230e-01 -4.13056582e-01 -3.44532728e-01
-5.58905125e-01 -1.45136461e-01 1.32204950e-01 9.19414222e-01
-7.33686686e-02 7.31417894e-01 6.31405354e-01 -5.37824869e-01
-4.31742698e-01 -8.86448264e-01 -8.41417074e-01 -4.60381061e-01
-7.75648057e-01 8.68199527e-01 -1.26496479e-01 -6.82515725... | [7.79809045791626, 6.621484756469727] |
5fdaccfd-cc10-4d83-aae3-10c9148fabd7 | italian-language-and-dialect-identification | null | null | https://aclanthology.org/2022.vardial-1.13 | https://aclanthology.org/2022.vardial-1.13.pdf | Italian Language and Dialect Identification and Regional French Variety Detection using Adaptive Naive Bayes | This article describes the language identification approach used by the SUKI team in the Identification of Languages and Dialects of Italy and the French Cross-Domain Dialect Identification shared tasks organized as part of the VarDial workshop 2022. We describe some experiments and the preprocessing techniques we used... | ['Krister Lindén', 'Heidi Jauhiainen', 'Tommi Jauhiainen'] | null | null | null | null | vardial-coling-2022-10 | ['dialect-identification'] | ['natural-language-processing'] | [-5.76070249e-01 -5.14668167e-01 -7.16383830e-02 -7.21899092e-01
-1.03312528e+00 -1.15078723e+00 8.18460524e-01 6.01588041e-02
-7.65880346e-01 7.41849601e-01 5.32029271e-01 -5.23428917e-01
-1.45238936e-01 -2.02672660e-01 9.50317383e-02 -3.66100997e-01
1.44606471e-01 1.16221178e+00 1.08460560e-02 -4.74717557... | [10.194891929626465, 10.729784965515137] |
80419917-83a0-4d63-8144-419200014bbc | plex-towards-reliability-using-pretrained | 2207.07411 | null | https://arxiv.org/abs/2207.07411v1 | https://arxiv.org/pdf/2207.07411v1.pdf | Plex: Towards Reliability using Pretrained Large Model Extensions | A recent trend in artificial intelligence is the use of pretrained models for language and vision tasks, which have achieved extraordinary performance but also puzzling failures. Probing these models' abilities in diverse ways is therefore critical to the field. In this paper, we explore the reliability of models, wher... | ['Balaji Lakshminarayanan', 'Jasper Snoek', 'Zoubin Ghahramani', 'Yarin Gal', 'D. Sculley', 'Kevin Murphy', 'Kelly Buchanan', 'Honglin Yuan', 'Nithum Thain', 'Rodolphe Jenatton', 'Andreas Kirsch', 'Joost van Amersfoort', 'Zachary Nado', 'Karan Singhal', 'Tim G. J. Rudner', 'Neil Band', 'Huiyi Hu', 'Zelda Mariet', 'Zi W... | 2022-07-15 | null | null | null | null | ['open-set-learning'] | ['miscellaneous'] | [-1.44852251e-01 9.16946307e-02 -7.32995197e-02 -7.44165540e-01
-1.32567847e+00 -4.62367207e-01 7.37743199e-01 2.86651012e-02
-5.44337869e-01 7.44311810e-01 -1.07780974e-02 -2.20554069e-01
-2.35693499e-01 -3.35695922e-01 -6.95898712e-01 -5.52027464e-01
-1.51761025e-01 9.68868792e-01 2.24870667e-01 5.18118106... | [9.914098739624023, 2.509899616241455] |
44b83714-7eeb-441d-9dc7-a3153901a018 | to-bert-or-not-to-bert-comparing-speech-and | 2008.01551 | null | https://arxiv.org/abs/2008.01551v1 | https://arxiv.org/pdf/2008.01551v1.pdf | To BERT or Not To BERT: Comparing Speech and Language-based Approaches for Alzheimer's Disease Detection | Research related to automatically detecting Alzheimer's disease (AD) is important, given the high prevalence of AD and the high cost of traditional methods. Since AD significantly affects the content and acoustics of spontaneous speech, natural language processing and machine learning provide promising techniques for r... | ['Frank Rudzicz', 'Jekaterina Novikova', 'Aparna Balagopalan', 'Benjamin Eyre'] | 2020-07-26 | null | null | null | null | ['alzheimer-s-disease-detection'] | ['medical'] | [ 3.97083521e-01 -1.13265350e-01 8.76052007e-02 -4.81102675e-01
-1.48511827e+00 -3.40573817e-01 7.19415128e-01 3.74078125e-01
-8.37590575e-01 5.16776264e-01 7.89585292e-01 -2.12764159e-01
-1.07808590e-01 -5.01758039e-01 -2.96688497e-01 -1.21973366e-01
-3.14040601e-01 5.82089186e-01 2.64791876e-01 -1.84967086... | [13.91264820098877, 5.391956329345703] |
a4297c20-35d3-40b2-b95a-f995a8155e2e | source-free-domain-adaptation-for-question | 2212.09563 | null | https://arxiv.org/abs/2212.09563v1 | https://arxiv.org/pdf/2212.09563v1.pdf | Source-Free Domain Adaptation for Question Answering with Masked Self-training | Most previous unsupervised domain adaptation (UDA) methods for question answering(QA) require access to source domain data while fine-tuning the model for the target domain. Source domain data may, however, contain sensitive information and may be restricted. In this study, we investigate a more challenging setting, so... | ['C. Ling', 'Y. Dong', 'B. Wang', 'M. Yin'] | 2022-12-19 | null | null | null | null | ['source-free-domain-adaptation'] | ['computer-vision'] | [ 5.77803612e-01 3.54897827e-01 -1.67195693e-01 -8.23096991e-01
-1.17015231e+00 -9.46647227e-01 4.37612265e-01 1.91252708e-01
-5.25620282e-01 8.56110394e-01 1.88070580e-01 -1.39839038e-01
2.15577424e-01 -9.34487104e-01 -8.24129879e-01 -3.74439180e-01
5.26566505e-01 9.88285363e-01 6.67525291e-01 -7.56668270... | [11.279884338378906, 7.93304443359375] |
68645924-4d12-416b-a855-3a256ab779c2 | free-bits-latency-optimization-of-mixed | 2307.02894 | null | https://arxiv.org/abs/2307.02894v1 | https://arxiv.org/pdf/2307.02894v1.pdf | Free Bits: Latency Optimization of Mixed-Precision Quantized Neural Networks on the Edge | Mixed-precision quantization, where a deep neural network's layers are quantized to different precisions, offers the opportunity to optimize the trade-offs between model size, latency, and statistical accuracy beyond what can be achieved with homogeneous-bit-width quantization. To navigate the intractable search space ... | ['Luca Benini', 'Francesco Conti', 'Georg Rutishauser'] | 2023-07-06 | null | null | null | null | ['quantization', 'navigate'] | ['methodology', 'reasoning'] | [ 9.97624919e-02 -2.39072412e-01 -3.87161255e-01 -5.85510075e-01
-8.57468903e-01 -4.10548210e-01 2.31288210e-01 3.74288648e-01
-1.18009412e+00 4.54147458e-01 -4.94831979e-01 -8.32094669e-01
-3.16458046e-02 -6.89467907e-01 -8.50492299e-01 -4.00093198e-01
-3.15039247e-01 2.80576378e-01 3.87427032e-01 -9.12717432... | [8.502299308776855, 2.975954532623291] |
d018e066-e638-4407-bb0a-696bca48f281 | a-holistic-approach-for-rapid-development-of | 2201.13243 | null | https://arxiv.org/abs/2201.13243v2 | https://arxiv.org/pdf/2201.13243v2.pdf | FastIoT -- A framework and holistic approach for rapid development of IIoT systems | While lots of research has been conducted on the architecture of Industrial Internet of Things (IIoT) systems, concepts of structuring their development processes are missing. Therefore, we propose a holistic approach supporting organizations in rapid development of IIoT systems. It includes the structuring of the deve... | ['Tilman Klaeger', 'Konstantin Merker'] | 2022-01-31 | null | null | null | null | ['miscellaneous'] | ['miscellaneous'] | [-5.23635745e-01 7.64671415e-02 1.39479637e-01 -5.62107623e-01
3.23319077e-01 -6.67700768e-01 2.40085781e-01 -2.27162406e-01
4.30018216e-01 3.05020481e-01 -2.98378646e-01 -6.17407024e-01
-3.91967773e-01 -8.14274669e-01 1.17993457e-02 -1.05088271e-01
6.46456838e-01 4.31851387e-01 5.82668483e-01 -1.87104046... | [8.565262794494629, 6.987950801849365] |
ea9ac861-a6e9-4d71-9396-41f6d7875a7a | seeking-an-optimal-approach-for-computer | 2109.07029 | null | https://arxiv.org/abs/2109.07029v1 | https://arxiv.org/pdf/2109.07029v1.pdf | Seeking an Optimal Approach for Computer-Aided Pulmonary Embolism Detection | Pulmonary embolism (PE) represents a thrombus ("blood clot"), usually originating from a lower extremity vein, that travels to the blood vessels in the lung, causing vascular obstruction and in some patients, death. This disorder is commonly diagnosed using CT pulmonary angiography (CTPA). Deep learning holds great pro... | ['Jianming Liang', 'Michael B Gotway', 'Zongwei Zhou', 'Shiv Gehlot', 'Nahid Ul Islam'] | 2021-09-15 | null | null | null | null | ['pulmonary-embolism-detection'] | ['medical'] | [ 5.35691194e-02 -1.04874253e-01 -1.51667252e-01 3.40404660e-01
-8.79060984e-01 -4.58317816e-01 4.76954430e-01 1.76452935e-01
-2.37400293e-01 6.82656229e-01 4.28183191e-02 -7.47686625e-01
-1.14958189e-01 -6.95723534e-01 -5.65925360e-01 -6.35789275e-01
-1.26440167e-01 7.40214944e-01 6.21844411e-01 4.18559551... | [15.159618377685547, -2.034687042236328] |
b92d415a-363f-420c-9e43-75e6ab43ab06 | continual-learning-for-seq2seq-generations | null | null | https://openreview.net/forum?id=yd7uyR9_0iU | https://openreview.net/pdf?id=yd7uyR9_0iU | Continual Learning for Seq2Seq Generations with Transformer Calibration | Conventional NLP generation models are trained offline with a given dataset for a particular task, which is referred to as isolated learning. Research on sequence-to-sequence language generation aims to study continual learning model to constantly learning from sequentially encountered tasks. However, continual learnin... | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['paraphrase-generation', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing'] | [ 3.06017876e-01 2.70734340e-01 -1.82795927e-01 -6.70548826e-02
-8.00373435e-01 -4.70844448e-01 7.31100559e-01 -6.97128922e-02
-3.16748261e-01 1.29718459e+00 2.64305681e-01 -3.60264868e-01
9.16583538e-02 -5.69975734e-01 -1.13626909e+00 -6.35236740e-01
5.94512463e-01 4.97718066e-01 4.41060551e-02 -3.36806148... | [9.902678489685059, 3.545225143432617] |
7f69f41a-7120-45f5-bcc6-ac88ed30e183 | autotamp-autoregressive-task-and-motion | 2306.06531 | null | https://arxiv.org/abs/2306.06531v1 | https://arxiv.org/pdf/2306.06531v1.pdf | AutoTAMP: Autoregressive Task and Motion Planning with LLMs as Translators and Checkers | For effective human-robot interaction, robots need to understand, plan, and execute complex, long-horizon tasks described by natural language. The recent and remarkable advances in large language models (LLMs) have shown promise for translating natural language into robot action sequences for complex tasks. However, ma... | ['Chuchu Fan', 'Nicholas Roy', 'Yang Zhang', 'Jacob Arkin', 'Yongchao Chen'] | 2023-06-10 | null | null | null | null | ['motion-planning'] | ['robots'] | [ 5.19162536e-01 5.90746701e-01 9.12740733e-03 -2.98274070e-01
-1.02346194e+00 -7.32633948e-01 8.27217519e-01 4.60870713e-02
-5.23449838e-01 6.89009488e-01 5.81055939e-01 -4.85646784e-01
9.87179577e-02 -4.97240931e-01 -7.05480635e-01 -1.50806129e-01
7.49817267e-02 1.04149973e+00 2.40646198e-01 -2.05400348... | [4.45339822769165, 0.8558868765830994] |
8274c756-8385-47d7-9b29-6e68f8729fdb | naturalprover-grounded-mathematical-proof | 2205.1291 | null | https://arxiv.org/abs/2205.12910v2 | https://arxiv.org/pdf/2205.12910v2.pdf | NaturalProver: Grounded Mathematical Proof Generation with Language Models | Theorem proving in natural mathematical language - the mixture of symbolic and natural language used by humans - plays a central role in mathematical advances and education, and tests aspects of reasoning that are core to intelligence. Yet it has remained underexplored with modern generative models. We study large-scal... | ['Yejin Choi', 'Hannaneh Hajishirzi', 'Ximing Lu', 'Jiacheng Liu', 'Sean Welleck'] | 2022-05-25 | null | null | null | null | ['automated-theorem-proving', 'automated-theorem-proving'] | ['miscellaneous', 'reasoning'] | [ 9.86876786e-02 5.51437318e-01 -6.30061775e-02 -1.98497042e-01
-7.60173202e-01 -1.01036131e+00 1.09829783e+00 5.93099475e-01
-7.05861226e-02 1.01382363e+00 -1.45476520e-01 -1.35011339e+00
-3.53064001e-01 -1.11227965e+00 -1.12511182e+00 1.07580841e-01
-3.52409273e-01 6.81732476e-01 1.20275766e-01 -3.74919772... | [9.122386932373047, 7.144862174987793] |
22a19542-50c7-4392-9466-73d9de2b6fa0 | ecg-based-heart-arrhythmia-diagnosis-through | 2108.10226 | null | https://arxiv.org/abs/2108.10226v2 | https://arxiv.org/pdf/2108.10226v2.pdf | ECG-Based Heart Arrhythmia Diagnosis Through Attentional Convolutional Neural Networks | Electrocardiography (ECG) signal is a highly applied measurement for individual heart condition, and much effort have been endeavored towards automatic heart arrhythmia diagnosis based on machine learning. However, traditional machine learning models require large investment of time and effort for raw data preprocessin... | ['Xiang Zhang', 'Ziyu Liu'] | 2021-08-18 | null | null | null | null | ['arrhythmia-detection', 'electrocardiography-ecg'] | ['medical', 'methodology'] | [ 2.48743027e-01 -1.02879040e-01 2.84727335e-01 -5.08919299e-01
-6.93947434e-01 -2.04566121e-01 -1.26256064e-01 1.62977278e-01
-5.68936504e-02 5.85895896e-01 1.52624846e-01 -4.64110970e-01
-3.57607901e-01 -3.42488289e-01 -2.07557663e-01 -6.52541459e-01
-3.45219404e-01 2.14906961e-01 -6.16443932e-01 1.53316319... | [14.292008399963379, 3.2737467288970947] |
457af7a1-2d86-4cfb-a974-d54b93c0cb47 | gui-at-mixmt-2022-english-hinglish-an-mt | 2210.12215 | null | https://arxiv.org/abs/2210.12215v1 | https://arxiv.org/pdf/2210.12215v1.pdf | Gui at MixMT 2022 : English-Hinglish: An MT approach for translation of code mixed data | Code-mixed machine translation has become an important task in multilingual communities and extending the task of machine translation to code mixed data has become a common task for these languages. In the shared tasks of WMT 2022, we try to tackle the same for both English + Hindi to Hinglish and Hinglish to English. ... | ['Vasudeva Varma', 'Dipti Misra Sharma', 'Tanvi Kamble', 'Saransh Rajput', 'Shivam Mangale', 'Anshul Padhi', 'Jayant Duneja', 'Akshat Gahoi'] | 2022-10-21 | null | null | null | null | ['transliteration'] | ['natural-language-processing'] | [-2.06409901e-01 -1.09462015e-01 -2.15524770e-02 -2.78878689e-01
-1.50234091e+00 -9.37402725e-01 8.25390875e-01 -3.56545508e-01
-6.07811451e-01 1.17724276e+00 4.52305414e-02 -8.24848056e-01
4.05171365e-01 -1.94802970e-01 -7.42740870e-01 -3.43876958e-01
2.67792702e-01 8.70562196e-01 -1.00646891e-01 -6.91637039... | [11.426462173461914, 10.406242370605469] |
60b87f6c-430c-48e8-95e2-17e018b7bdcb | sudo-rm-rf-efficient-networks-for-universal | 2007.06833 | null | https://arxiv.org/abs/2007.06833v1 | https://arxiv.org/pdf/2007.06833v1.pdf | Sudo rm -rf: Efficient Networks for Universal Audio Source Separation | In this paper, we present an efficient neural network for end-to-end general purpose audio source separation. Specifically, the backbone structure of this convolutional network is the SUccessive DOwnsampling and Resampling of Multi-Resolution Features (SuDoRMRF) as well as their aggregation which is performed through s... | ['Paris Smaragdis', 'Zhepei Wang', 'Efthymios Tzinis'] | 2020-07-14 | null | null | null | null | ['audio-source-separation'] | ['audio'] | [-8.46061576e-03 -8.25862825e-01 5.46543896e-01 -1.90057769e-01
-8.88205051e-01 -4.67534363e-01 2.59315282e-01 1.45071536e-01
-6.62474036e-01 5.56712925e-01 1.81984365e-01 -1.78394899e-01
-2.76824921e-01 -6.60040021e-01 -4.91956294e-01 -6.31343544e-01
-2.80804485e-01 -2.02509031e-01 3.14326316e-01 -3.32236588... | [15.318681716918945, 5.5504536628723145] |
4eacf385-ef25-48f3-9748-8dd18fce7492 | automated-pavement-crack-segmentation-using | 2001.01912 | null | https://arxiv.org/abs/2001.01912v4 | https://arxiv.org/pdf/2001.01912v4.pdf | Automated Pavement Crack Segmentation Using U-Net-based Convolutional Neural Network | Automated pavement crack image segmentation is challenging because of inherent irregular patterns, lighting conditions, and noise in images. Conventional approaches require a substantial amount of feature engineering to differentiate crack regions from non-affected regions. In this paper, we propose a deep learning tec... | ['Stephen L. H. Lau', 'Edwin K. P. Chong', 'Xu Yang', 'Xin Wang'] | 2020-01-07 | null | null | null | null | ['crack-segmentation'] | ['computer-vision'] | [ 4.02452022e-01 -9.44911391e-02 4.27837104e-01 -3.30490589e-01
-6.67118669e-01 -3.78217727e-01 2.02648550e-01 -9.33827534e-02
-6.22080922e-01 2.32865214e-01 -4.25506949e-01 -5.38038552e-01
2.71261454e-01 -1.04471374e+00 -9.27756071e-01 -5.34797668e-01
6.39397278e-02 9.37884971e-02 5.95284224e-01 -3.19437146... | [7.4985270500183105, 1.6102079153060913] |
ea6fef51-cc8c-4b1f-802d-0e815e88c556 | optical-flow-based-real-time-moving-object | 1807.0489 | null | http://arxiv.org/abs/1807.04890v1 | http://arxiv.org/pdf/1807.04890v1.pdf | Optical Flow Based Real-time Moving Object Detection in Unconstrained Scenes | Real-time moving object detection in unconstrained scenes is a difficult task
due to dynamic background, changing foreground appearance and limited
computational resource. In this paper, an optical flow based moving object
detection framework is proposed to address this problem. We utilize homography
matrixes to online... | ['Zheng Zhu', 'Wei Zou', 'Junjie Huang', 'Jiagang Zhu'] | 2018-07-13 | null | null | null | null | ['moving-object-detection'] | ['computer-vision'] | [ 1.28525257e-01 -8.72915089e-01 -1.32945627e-01 -7.00252056e-02
2.30297986e-02 -4.78460133e-01 4.03121591e-01 -8.43560517e-01
-3.78899336e-01 6.88373864e-01 -1.31834999e-01 -2.42967874e-01
1.22144334e-01 -5.08313656e-01 -2.43595809e-01 -7.31932461e-01
3.02072358e-03 -3.18767101e-01 9.55106616e-01 1.65490031... | [9.008204460144043, -0.688030481338501] |
f3e880b8-5871-43f6-8301-9cd0af97bdd6 | automatic-debiased-learning-from-positive | 2303.04797 | null | https://arxiv.org/abs/2303.04797v1 | https://arxiv.org/pdf/2303.04797v1.pdf | Automatic Debiased Learning from Positive, Unlabeled, and Exposure Data | We address the issue of binary classification from positive and unlabeled data (PU classification) with a selection bias in the positive data. During the observation process, (i) a sample is exposed to a user, (ii) the user then returns the label for the exposed sample, and (iii) we however can only observe the positiv... | ['Shota Yasui', 'Kodai Kureishi', 'Shuting Wu', 'Masahiro Kato'] | 2023-03-08 | null | null | null | null | ['selection-bias'] | ['natural-language-processing'] | [ 4.60732102e-01 7.48469755e-02 -8.95122409e-01 -5.07665873e-01
-9.60334599e-01 -6.09844267e-01 5.12396753e-01 2.58642614e-01
-4.30578738e-01 9.19847369e-01 -1.86430976e-01 -4.91577893e-01
-9.20018777e-02 -1.05122364e+00 -9.39376473e-01 -9.72359538e-01
1.67469367e-01 5.79462826e-01 -5.47069535e-02 3.29564482... | [9.119989395141602, 4.176442623138428] |
07818e3f-5834-4c13-a210-05abf0276653 | histogram-of-oriented-principal-components | 1409.6813 | null | http://arxiv.org/abs/1409.6813v2 | http://arxiv.org/pdf/1409.6813v2.pdf | Histogram of Oriented Principal Components for Cross-View Action Recognition | Existing techniques for 3D action recognition are sensitive to viewpoint
variations because they extract features from depth images which are viewpoint
dependent. In contrast, we directly process pointclouds for cross-view action
recognition from unknown and unseen views. We propose the Histogram of Oriented
Principal ... | ['Arif Mahmood', 'Ajmal Mian', 'Hossein Rahmani', 'Du Huynh'] | 2014-09-24 | null | null | null | null | ['3d-human-action-recognition'] | ['computer-vision'] | [ 1.08744271e-01 -8.02983046e-01 -1.76179111e-01 5.98492287e-02
-6.59861803e-01 -5.89360178e-01 6.66288972e-01 3.27889398e-02
-2.54875273e-01 1.09153621e-01 3.79343063e-01 3.35646480e-01
-1.99623734e-01 -3.93331915e-01 -2.96020538e-01 -8.52197826e-01
-1.69958353e-01 2.74043232e-01 8.84544075e-01 -5.39065301... | [7.966944694519043, 0.2724151611328125] |
c07bdcb0-7d48-4c2f-8c00-3cf4e0dca8d6 | on-the-trajectory-of-stochastic-gradient | null | null | https://openreview.net/forum?id=SkMON20ctX | https://openreview.net/pdf?id=SkMON20ctX | On the Trajectory of Stochastic Gradient Descent in the Information Plane | Studying the evolution of information theoretic quantities during Stochastic Gradient Descent (SGD) learning of Artificial Neural Networks (ANNs) has gained popularity in recent years.
Nevertheless, these type of experiments require estimating mutual information and entropy which becomes intractable for moderately lar... | ['Rudolf Mathar', 'Arash Behboodi', 'Emilio Rafael Balda'] | 2019-05-01 | null | null | null | iclr-2019-5 | ['information-plane'] | ['methodology'] | [-1.21353768e-01 3.20332915e-01 9.29048583e-02 -3.51331800e-01
-9.99091715e-02 -4.86719698e-01 6.57749176e-01 4.13104117e-01
-5.11621118e-01 5.90151966e-01 -3.62237275e-01 -3.43453407e-01
-4.92641360e-01 -5.86570978e-01 -5.34537137e-01 -1.04200256e+00
-5.10898411e-01 4.19529736e-01 -1.60718977e-01 1.81121245... | [7.848017692565918, 3.6190359592437744] |
de8618fc-4cc9-44f1-b604-ac715b92149a | capot-creating-robust-dense-query-encoders | 2304.03401 | null | https://arxiv.org/abs/2304.03401v2 | https://arxiv.org/pdf/2304.03401v2.pdf | Noise-Robust Dense Retrieval via Contrastive Alignment Post Training | The success of contextual word representations and advances in neural information retrieval have made dense vector-based retrieval a standard approach for passage and document ranking. While effective and efficient, dual-encoders are brittle to variations in query distributions and noisy queries. Data augmentation can ... | ['Alessandro Magnani', 'ChengXiang Zhai', 'Daniel Campos'] | 2023-04-06 | null | null | null | null | ['natural-questions', 'document-ranking', 'passage-retrieval'] | ['miscellaneous', 'natural-language-processing', 'natural-language-processing'] | [ 2.47576565e-01 -2.52064615e-01 -2.71439403e-01 -1.84666991e-01
-1.81834555e+00 -8.62283587e-01 9.48120475e-01 5.21539509e-01
-8.23598027e-01 8.20948482e-01 7.62573242e-01 -2.63385803e-01
-1.86388239e-01 -4.91280675e-01 -8.03113520e-01 -3.99040043e-01
-2.97142025e-02 9.14325297e-01 1.55222028e-01 -6.74790025... | [11.501824378967285, 7.6722025871276855] |
dec04177-bdbb-485e-b895-df5881e801a2 | panoramic-annular-slam-with-loop-closure-and | 2102.134 | null | https://arxiv.org/abs/2102.13400v2 | https://arxiv.org/pdf/2102.13400v2.pdf | Panoramic annular SLAM with loop closure and global optimization | In this paper, we propose panoramic annular simultaneous localization and mapping (PA-SLAM), a visual SLAM system based on panoramic annular lens. A hybrid point selection strategy is put forward in the tracking front-end, which ensures repeatability of keypoints and enables loop closure detection based on the bag-of-w... | ['Kaiwei Wang', 'Jian Bai', 'Kailun Yang', 'Weijian Hu', 'Hao Chen'] | 2021-02-26 | null | null | null | null | ['loop-closure-detection'] | ['computer-vision'] | [-1.90653697e-01 -2.24598497e-01 -1.05943277e-01 -1.69366732e-01
-5.29995143e-01 -6.22587621e-01 7.14236677e-01 1.51066855e-01
-5.86345851e-01 4.87309903e-01 -4.97739047e-01 -2.17660785e-01
-2.26396188e-01 -6.01177275e-01 -8.40073466e-01 -1.68449238e-01
-4.54316556e-01 3.95160139e-01 4.48584646e-01 -1.61879435... | [7.411942005157471, -2.172227382659912] |
499a5c6d-7706-4a1e-bf67-e0a44ab27ef2 | haca3-a-unified-approach-for-multi-site-mr | 2212.06065 | null | https://arxiv.org/abs/2212.06065v2 | https://arxiv.org/pdf/2212.06065v2.pdf | HACA3: A Unified Approach for Multi-site MR Image Harmonization | The lack of standardization is a prominent issue in magnetic resonance (MR) imaging. This often causes undesired contrast variations in the acquired images due to differences in hardware and acquisition parameters. In recent years, image synthesis-based MR harmonization with disentanglement has been proposed to compens... | ['Murat Bilgel', 'Savannah P. Hays', 'Samuel W. Remedios', 'Aaron Carass', 'Jerry L. Prince', 'Susan M. Resnick', 'Peter A. Calabresi', 'Scott D. Newsome', 'Ellen M. Mowry', 'Blake E. Dewey', 'Yuan Xue', 'Yihao Liu', 'Lianrui Zuo'] | 2022-12-12 | null | null | null | null | ['image-harmonization'] | ['computer-vision'] | [ 3.11727881e-01 -3.63720983e-01 -4.92601432e-02 -3.22257578e-01
-8.79222274e-01 -4.20845330e-01 3.39502573e-01 2.56996959e-01
-4.38264787e-01 6.03278518e-01 1.86900139e-01 -1.34529233e-01
-3.18186343e-01 -3.58793288e-01 -3.77321422e-01 -5.80868244e-01
-3.53752315e-01 2.68607467e-01 5.24186432e-01 -6.64699823... | [13.859962463378906, -2.335200071334839] |
951a4f8b-db72-443a-9e4f-09bc373bf4f7 | chain-of-thought-imitation-with-procedure | 2205.10816 | null | https://arxiv.org/abs/2205.10816v1 | https://arxiv.org/pdf/2205.10816v1.pdf | Chain of Thought Imitation with Procedure Cloning | Imitation learning aims to extract high-performance policies from logged demonstrations of expert behavior. It is common to frame imitation learning as a supervised learning problem in which one fits a function approximator to the input-output mapping exhibited by the logged demonstrations (input observations to output... | ['Ofir Nachum', 'Pieter Abbeel', 'Dale Schuurmans', 'Mengjiao Yang'] | 2022-05-22 | null | null | null | null | ['robot-manipulation'] | ['robots'] | [ 3.42218161e-01 3.60301942e-01 -2.23152146e-01 -1.86057128e-02
-5.24616957e-01 -1.18824065e+00 6.78000093e-01 -1.92527547e-01
-4.28012609e-01 7.60623336e-01 -3.69154543e-01 -1.00720739e+00
-6.37157708e-02 -6.59311473e-01 -1.15025103e+00 -5.75838804e-01
-1.66472912e-01 4.89912808e-01 2.08581984e-01 -2.04609036... | [4.256078243255615, 1.4609465599060059] |
e65b15ed-66ca-40c2-8e27-2e6937e775df | open-unmix-a-reference-implementation-for | null | null | https://joss.theoj.org/papers/10.21105/joss.01667 | https://www.theoj.org/joss-papers/joss.01667/10.21105.joss.01667.pdf | Open-Unmix - A Reference Implementation for Music Source Separation | Music source separation is the task of decomposing music into its constitutive components,e.g., yielding separated stems for the vocals, bass, and drums. Such a separation has manyapplications ranging from rearranging/repurposing the stems (remixing, repanning, upmixing)to full extraction (karaoke, sample creation, aud... | ['and YukiMitsufuji', 'Fabian-Robert Stöter', 'Stefan Uhlich', 'Antoine Liutkus'] | 2019-09-08 | null | null | null | the-journal-of-open-source-software-2019-9 | ['music-source-separation'] | ['music'] | [-1.70231074e-01 -4.30732161e-01 -2.38828212e-01 3.41021836e-01
-9.23081696e-01 -9.38354075e-01 3.86660606e-01 -3.47753048e-01
-6.50707334e-02 5.00337362e-01 2.06729650e-01 -1.04931198e-01
-1.96179494e-01 -4.22166824e-01 -5.16444564e-01 -8.12274277e-01
-1.72295347e-01 2.89658904e-01 -9.18415189e-02 -2.30701044... | [15.68738079071045, 5.398838520050049] |
dcd76bb9-5efc-41cd-8c59-3af9753be579 | global-convergence-using-policy-gradient | 2111.15228 | null | https://arxiv.org/abs/2111.15228v1 | https://arxiv.org/pdf/2111.15228v1.pdf | Global Convergence Using Policy Gradient Methods for Model-free Markovian Jump Linear Quadratic Control | Owing to the growth of interest in Reinforcement Learning in the last few years, gradient based policy control methods have been gaining popularity for Control problems as well. And rightly so, since gradient policy methods have the advantage of optimizing a metric of interest in an end-to-end manner, along with being ... | ['Abir De', 'Manoj Bhadu', 'Santanu Rathod'] | 2021-11-30 | null | null | null | null | ['policy-gradient-methods'] | ['methodology'] | [-2.02896968e-01 -5.23308180e-02 -3.90952349e-01 1.87508196e-01
-6.57096744e-01 -4.96701807e-01 5.23093104e-01 1.69824943e-01
-7.18654513e-01 1.32163393e+00 5.85001940e-03 -7.17571378e-01
-2.54085809e-01 -2.97616303e-01 -5.89992642e-01 -6.80112660e-01
-3.62626165e-01 3.31624448e-01 -5.59423119e-02 -4.77836996... | [4.403172016143799, 2.5218863487243652] |
9bd9973c-d4a2-4123-b555-8ceeaa5df760 | consensus-based-networked-tracking-in | 2302.07511 | null | https://arxiv.org/abs/2302.07511v1 | https://arxiv.org/pdf/2302.07511v1.pdf | Consensus-based Networked Tracking in Presence of Heterogeneous Time-Delays | We propose a distributed (single) target tracking scheme based on networked estimation and consensus algorithms over static sensor networks. The tracking part is based on linear time-difference-of-arrival (TDOA) measurement proposed in our previous works. This paper, in particular, develops delay-tolerant distributed f... | ['Usman A. Khan', 'Mohammad Pirani', 'Mohammadreza Doostmohammadian'] | 2023-02-15 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [-4.65241894e-02 1.61405742e-01 9.06863809e-02 -8.34784806e-02
-2.66412228e-01 -8.99891376e-01 2.02715605e-01 4.11831141e-01
-6.62398487e-02 9.32640672e-01 -3.12269956e-01 -1.15940072e-01
-9.63743567e-01 -9.53974128e-01 -6.23190939e-01 -1.01330411e+00
-1.07986903e+00 3.83641213e-01 7.32519090e-01 -2.27284044... | [5.848665237426758, 1.5362756252288818] |
b64626d7-77d0-4505-a3e8-654a37f0d66e | freegaze-resource-efficient-gaze-estimation | 2209.06692 | null | https://arxiv.org/abs/2209.06692v1 | https://arxiv.org/pdf/2209.06692v1.pdf | FreeGaze: Resource-efficient Gaze Estimation via Frequency Domain Contrastive Learning | Gaze estimation is of great importance to many scientific fields and daily applications, ranging from fundamental research in cognitive psychology to attention-aware mobile systems. While recent advancements in deep learning have yielded remarkable successes in building highly accurate gaze estimation systems, the asso... | ['Guohao Lan', 'Lingyu Du'] | 2022-09-14 | null | null | null | null | ['gaze-estimation'] | ['computer-vision'] | [ 2.11853787e-01 -5.42567968e-02 -2.31244266e-01 -4.37971592e-01
-5.96532583e-01 -2.20093802e-01 7.02586770e-02 -6.46761619e-03
-4.46479023e-01 5.29701114e-01 -1.95351407e-01 -4.57625926e-01
-2.33624846e-01 -1.79271445e-01 -4.12703574e-01 -7.52816021e-01
2.67337024e-01 -1.15546539e-01 1.54420108e-01 -4.57323194... | [14.11955738067627, 0.08026440441608429] |
319b38be-6904-463b-ad6d-99dda12b2094 | foreground-action-consistency-network-for | 2108.06524 | null | https://arxiv.org/abs/2108.06524v1 | https://arxiv.org/pdf/2108.06524v1.pdf | Foreground-Action Consistency Network for Weakly Supervised Temporal Action Localization | As a challenging task of high-level video understanding, weakly supervised temporal action localization has been attracting increasing attention. With only video annotations, most existing methods seek to handle this task with a localization-by-classification framework, which generally adopts a selector to select snipp... | ['Hongsheng Li', 'Liang Wang', 'Linjiang Huang'] | 2021-08-14 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Huang_Foreground-Action_Consistency_Network_for_Weakly_Supervised_Temporal_Action_Localization_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Huang_Foreground-Action_Consistency_Network_for_Weakly_Supervised_Temporal_Action_Localization_ICCV_2021_paper.pdf | iccv-2021-1 | ['weakly-supervised-temporal-action'] | ['computer-vision'] | [ 3.91625911e-01 -1.69771925e-01 -7.05762982e-01 -2.18011811e-01
-5.18994570e-01 -1.38504073e-01 5.84866822e-01 -3.46408695e-01
-1.49985656e-01 4.24831808e-01 2.14671388e-01 -6.72747344e-02
4.01740335e-03 -6.08385205e-01 -6.04133546e-01 -1.02256131e+00
1.04280837e-01 8.20639282e-02 1.08557320e+00 3.07787180... | [8.49557876586914, 0.6422508955001831] |
d64241bc-5bf3-49f1-bdad-e907ec6fce28 | high-dimensional-clustering-onto-hamiltonian | 2304.14531 | null | https://arxiv.org/abs/2304.14531v2 | https://arxiv.org/pdf/2304.14531v2.pdf | High-dimensional Clustering onto Hamiltonian Cycle | Clustering aims to group unlabelled samples based on their similarities. It has become a significant tool for the analysis of high-dimensional data. However, most of the clustering methods merely generate pseudo labels and thus are unable to simultaneously present the similarities between different clusters and outlier... | ['Zhengjun Zhang', 'Stan Z. Li', 'Shenghui Cheng', 'Tianyi Huang'] | 2023-04-27 | null | null | null | null | ['deep-clustering', 'deep-clustering'] | ['miscellaneous', 'natural-language-processing'] | [-3.42008471e-01 1.02347367e-01 8.00528973e-02 -3.25476408e-01
-5.12998939e-01 -4.06937152e-01 3.87520134e-01 5.37241936e-01
-1.34812027e-01 2.69436419e-01 5.63350953e-02 2.60210335e-01
-3.25624377e-01 -7.65160203e-01 -2.08766907e-01 -1.20559633e+00
-2.25089014e-01 6.60533249e-01 2.56573796e-01 3.17166448... | [7.617795944213867, 4.602367401123047] |
de2cac35-5cd6-4a45-a7da-44ee6293f192 | enhancing-local-feature-learning-for-3d-point | 2203.00172 | null | https://arxiv.org/abs/2203.00172v2 | https://arxiv.org/pdf/2203.00172v2.pdf | Enhancing Local Feature Learning for 3D Point Cloud Processing using Unary-Pairwise Attention | We present a simple but effective attention named the unary-pairwise attention (UPA) for modeling the relationship between 3D point clouds. Our idea is motivated by the analysis that the standard self-attention (SA) that operates globally tends to produce almost the same attention maps for different query positions, re... | ['Weimin WANG', 'Masashi Matsuoka', 'Qiong Chang', 'Takayuki Shinohara', 'Kyoung-Sook Kim', 'Xin Liu', 'Haoyi Xiu'] | 2022-03-01 | null | null | null | null | ['scene-segmentation'] | ['computer-vision'] | [-1.42813623e-01 -2.89705954e-02 -1.77877340e-02 -3.45037878e-01
-8.54359388e-01 -5.77039838e-01 6.21024430e-01 2.03869283e-01
-1.29436195e-01 4.65377010e-02 -4.58377860e-02 -3.07519525e-01
-2.43960127e-01 -7.18229055e-01 -1.26921082e+00 -4.52891737e-01
1.66566864e-01 8.81443143e-01 5.64275384e-01 -3.26208681... | [7.955864429473877, -3.4633729457855225] |
1a540a51-0274-43f2-a1cc-dd24cae21890 | a-decade-survey-of-content-based-image | 2012.00641 | null | https://arxiv.org/abs/2012.00641v2 | https://arxiv.org/pdf/2012.00641v2.pdf | A Decade Survey of Content Based Image Retrieval using Deep Learning | The content based image retrieval aims to find the similar images from a large scale dataset against a query image. Generally, the similarity between the representative features of the query image and dataset images is used to rank the images for retrieval. In early days, various hand designed feature descriptors have ... | ['Shiv Ram Dubey'] | 2020-11-23 | null | null | null | null | ['content-based-image-retrieval'] | ['computer-vision'] | [-1.36871636e-01 -7.59256005e-01 -3.81924599e-01 -5.65479875e-01
-9.14121270e-01 -3.58104050e-01 7.48457551e-01 3.34282041e-01
-3.68607342e-01 1.27282232e-01 -2.28002053e-02 5.81510067e-01
-7.67950177e-01 -7.14213312e-01 -2.62051105e-01 -8.91225874e-01
-1.11846484e-01 2.87449241e-01 1.71274003e-02 -3.44652653... | [10.760590553283691, 0.46393418312072754] |
65b219bb-fb6d-4236-9521-07e1a45a29ed | intra-template-entity-compatibility-based | null | null | https://aclanthology.org/2022.bionlp-1.18 | https://aclanthology.org/2022.bionlp-1.18.pdf | Intra-Template Entity Compatibility based Slot-Filling for Clinical Trial Information Extraction | We present a deep learning based information extraction system that can extract the design and results of a published abstract describing a Randomized Controlled Trial (RCT). In contrast to other approaches, our system does not regard the PICO elements as flat objects or labels but as structured objects. We thus model ... | ['Philipp Cimiano', 'Christian Witte'] | null | null | null | null | bionlp-acl-2022-5 | ['pico', 'slot-filling'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.67214137e-01 5.49520075e-01 -7.35336959e-01 -5.51308393e-01
-1.17549133e+00 -5.45827985e-01 4.29325014e-01 9.00595486e-01
-5.74392617e-01 8.47697139e-01 4.78085309e-01 -7.07564592e-01
-5.53534091e-01 -5.51606297e-01 -7.63990521e-01 -4.06675130e-01
-2.53881495e-02 8.67295921e-01 1.08315185e-01 4.58599091... | [8.470317840576172, 8.673316955566406] |
af10dea8-0619-4d3c-8a20-ab07c81da55f | a-differentiable-gaussian-prototype-layer-for | 2306.14361 | null | https://arxiv.org/abs/2306.14361v1 | https://arxiv.org/pdf/2306.14361v1.pdf | A differentiable Gaussian Prototype Layer for explainable Segmentation | We introduce a Gaussian Prototype Layer for gradient-based prototype learning and demonstrate two novel network architectures for explainable segmentation one of which relies on region proposals. Both models are evaluated on agricultural datasets. While Gaussian Mixture Models (GMMs) have been used to model latent dist... | ['Sebastian Bosse', 'Peter Eisert', 'Steffen Maaß', 'Michael Gerstenberger'] | 2023-06-25 | null | null | null | null | ['superpixels'] | ['computer-vision'] | [ 4.86291870e-02 7.58323967e-01 -2.32640684e-01 -5.03723502e-01
-3.24066162e-01 -4.32588458e-01 6.37603521e-01 1.00909904e-01
-3.26655626e-01 3.41709256e-01 -4.31857377e-01 -4.64176863e-01
-4.30346467e-02 -8.84330511e-01 -9.28752422e-01 -7.38622665e-01
-1.40628472e-01 8.23472619e-01 6.67440295e-01 -3.10243331... | [9.50912094116211, 0.34689775109291077] |
4be4e6d2-fc96-440a-8ce5-d96f7227590d | unseen-object-instance-segmentation-for | 2007.08073 | null | https://arxiv.org/abs/2007.08073v2 | https://arxiv.org/pdf/2007.08073v2.pdf | Unseen Object Instance Segmentation for Robotic Environments | In order to function in unstructured environments, robots need the ability to recognize unseen objects. We take a step in this direction by tackling the problem of segmenting unseen object instances in tabletop environments. However, the type of large-scale real-world dataset required for this task typically does not e... | ['Yu Xiang', 'Dieter Fox', 'Christopher Xie', 'Arsalan Mousavian'] | 2020-07-16 | null | null | null | null | ['unseen-object-instance-segmentation'] | ['computer-vision'] | [ 6.86536133e-01 5.28936803e-01 2.36729130e-01 -3.07333678e-01
-6.53481305e-01 -1.00662243e+00 3.39854717e-01 -1.50738463e-01
-2.43168563e-01 4.17637646e-01 -5.25708199e-01 -1.09030604e-01
5.75808324e-02 -6.47529662e-01 -1.15634239e+00 -4.86834854e-01
2.34591156e-01 9.20738935e-01 5.72333157e-01 -3.26774180... | [6.135833740234375, -1.0511854887008667] |
d1c87cf7-9cc3-4ade-aaed-40c0c1c2801a | provably-efficient-gauss-newton-temporal | 2302.13087 | null | https://arxiv.org/abs/2302.13087v1 | https://arxiv.org/pdf/2302.13087v1.pdf | Provably Efficient Gauss-Newton Temporal Difference Learning Method with Function Approximation | In this paper, based on the spirit of Fitted Q-Iteration (FQI), we propose a Gauss-Newton Temporal Difference (GNTD) method to solve the Q-value estimation problem with function approximation. In each iteration, unlike the original FQI that solves a nonlinear least square subproblem to fit the Q-iteration, the GNTD met... | ['Junyu Zhang', 'Zaiwen Wen', 'Zhifa Ke'] | 2023-02-25 | null | null | null | null | ['offline-rl'] | ['playing-games'] | [-1.35441869e-01 1.64239019e-01 -1.02051422e-01 -9.49884802e-02
-1.09528005e+00 -3.39245319e-01 -2.29290336e-01 1.16675552e-02
-7.80062795e-01 1.07881176e+00 -5.05929708e-01 -6.49264038e-01
-4.22734678e-01 -6.44805968e-01 -1.05421770e+00 -7.09300697e-01
-2.47240156e-01 1.55793205e-01 -1.02104686e-01 -2.41045445... | [4.23995304107666, 2.645007371902466] |
8443b899-c349-49a7-866e-a087b60716a8 | functional-space-variational-inference-for | 2005.11797 | null | https://arxiv.org/abs/2005.11797v2 | https://arxiv.org/pdf/2005.11797v2.pdf | Functional Space Variational Inference for Uncertainty Estimation in Computer Aided Diagnosis | Deep neural networks have revolutionized medical image analysis and disease diagnosis. Despite their impressive performance, it is difficult to generate well-calibrated probabilistic outputs for such networks, which makes them uninterpretable black boxes. Bayesian neural networks provide a principled approach for model... | ['Hrushikesh Loya', 'Pranav Poduval', 'Amit Sethi'] | 2020-05-24 | null | https://openreview.net/forum?id=eLL-c_Xc0B | https://openreview.net/pdf?id=eLL-c_Xc0B | midl-2019-7 | ['skin-lesion-classification'] | ['medical'] | [ 2.06322700e-01 4.45440978e-01 -3.90677273e-01 -6.38413727e-01
-1.02005041e+00 -3.00644040e-01 2.55976140e-01 1.14340961e-01
-5.37266672e-01 9.90956545e-01 3.50985862e-02 -4.42708611e-01
-3.81925821e-01 -6.96147263e-01 -6.50212944e-01 -8.22288454e-01
1.29487365e-01 5.33376694e-01 2.91619990e-02 4.97311234... | [14.153109550476074, -2.086202383041382] |
3e8b715e-e8fb-4831-a7c3-088c374479f6 | confronting-abusive-language-online-a-survey | 2012.12305 | null | https://arxiv.org/abs/2012.12305v2 | https://arxiv.org/pdf/2012.12305v2.pdf | Confronting Abusive Language Online: A Survey from the Ethical and Human Rights Perspective | The pervasiveness of abusive content on the internet can lead to severe psychological and physical harm. Significant effort in Natural Language Processing (NLP) research has been devoted to addressing this problem through abusive content detection and related sub-areas, such as the detection of hate speech, toxicity, c... | ['Kathleen C. Fraser', 'Isar Nejadgholi', 'Svetlana Kiritchenko'] | 2020-12-22 | null | null | null | null | ['abuse-detection'] | ['natural-language-processing'] | [ 2.81120241e-01 2.75709685e-02 -1.79281920e-01 -2.83757418e-01
-1.51918635e-01 -8.79133880e-01 3.96551192e-01 3.50063324e-01
-4.53608632e-01 7.82626987e-01 5.61536074e-01 -3.55992585e-01
-3.63030583e-01 -3.58780801e-01 -2.13318601e-01 -2.79496253e-01
1.18032463e-01 -2.74100929e-01 -2.75615633e-01 -2.84004152... | [8.67236614227295, 10.333911895751953] |
49b4eada-e72a-440c-a95a-5ff9a77eee16 | magicvo-end-to-end-monocular-visual-odometry | 1811.10964 | null | http://arxiv.org/abs/1811.10964v2 | http://arxiv.org/pdf/1811.10964v2.pdf | MagicVO: End-to-End Monocular Visual Odometry through Deep Bi-directional Recurrent Convolutional Neural Network | This paper proposes a new framework to solve the problem of monocular visual
odometry, called MagicVO . Based on Convolutional Neural Network (CNN) and
Bi-directional LSTM (Bi-LSTM), MagicVO outputs a 6-DoF absolute-scale pose at
each position of the camera with a sequence of continuous monocular images as
input. It no... | ['Zhongliang Deng', 'Yaokai Mo', 'Weilun Liu', 'Jian Jiao', 'Jichao Jiao'] | 2018-11-27 | null | null | null | null | ['monocular-visual-odometry'] | ['robots'] | [-6.47583485e-01 -2.54888654e-01 -3.45408887e-01 -4.10840780e-01
2.06944883e-01 -1.09532379e-01 4.09746885e-01 -8.51292074e-01
-5.77527702e-01 4.97270584e-01 -9.21236649e-02 4.01436947e-02
3.63820642e-01 -4.85131413e-01 -1.02302313e+00 -2.75662094e-01
-6.22873157e-02 5.81544220e-01 2.80785739e-01 -2.41920426... | [8.082923889160156, -2.1520984172821045] |
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