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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
60c93d4e-3838-47dc-84d8-7718a4830cea | noisyhate-benchmarking-content-moderation | 2303.10430 | null | https://arxiv.org/abs/2303.10430v1 | https://arxiv.org/pdf/2303.10430v1.pdf | NoisyHate: Benchmarking Content Moderation Machine Learning Models with Human-Written Perturbations Online | Online texts with toxic content are a threat in social media that might cause cyber harassment. Although many platforms applied measures, such as machine learning-based hate-speech detection systems, to diminish their effect, those toxic content publishers can still evade the system by modifying the spelling of toxic w... | ['Dongwon Lee', 'Thai Le', 'Yiran Ye'] | 2023-03-18 | null | null | null | null | ['hate-speech-detection'] | ['natural-language-processing'] | [ 1.73788339e-01 5.09211328e-03 3.41011137e-01 1.16466001e-01
-6.18379056e-01 -1.01670861e+00 7.59934604e-01 -4.36888251e-04
-2.64757454e-01 6.09701574e-01 1.92784399e-01 -2.65762657e-01
4.77579981e-01 -7.50343502e-01 -8.82101893e-01 -5.89327991e-01
2.33324081e-01 4.56206724e-02 2.05311492e-01 -5.80697000... | [5.997866153717041, 8.07761001586914] |
1bcf2347-b687-4752-8c44-4bca44aebc84 | imojie-iterative-memory-based-joint-open | 2005.08178 | null | https://arxiv.org/abs/2005.08178v1 | https://arxiv.org/pdf/2005.08178v1.pdf | IMoJIE: Iterative Memory-Based Joint Open Information Extraction | While traditional systems for Open Information Extraction were statistical and rule-based, recently neural models have been introduced for the task. Our work builds upon CopyAttention, a sequence generation OpenIE model (Cui et. al., 2018). Our analysis reveals that CopyAttention produces a constant number of extractio... | ['Soumen Chakrabarti', 'Mausam', 'Keshav Kolluru', 'Vipul Rathore', 'Samarth Aggarwal'] | 2020-05-17 | imojie-iterative-memory-based-joint-open-1 | https://aclanthology.org/2020.acl-main.521 | https://aclanthology.org/2020.acl-main.521.pdf | acl-2020-6 | ['open-information-extraction'] | ['natural-language-processing'] | [ 2.56423920e-01 8.74212444e-01 -8.37692060e-03 -1.12951092e-01
-1.03593814e+00 -8.04034352e-01 5.76922178e-01 1.98514476e-01
-4.76030499e-01 1.26880753e+00 4.33206737e-01 -1.48233518e-01
3.59083340e-02 -8.58060479e-01 -1.04769301e+00 -6.04373962e-02
4.59846994e-03 6.24875426e-01 -1.22392969e-02 -2.50668883... | [9.82254695892334, 8.758051872253418] |
92a17130-d4d4-482a-84b6-46256e4c74c2 | demystify-transformers-convolutions-in-modern | 2211.05781 | null | https://arxiv.org/abs/2211.05781v1 | https://arxiv.org/pdf/2211.05781v1.pdf | Demystify Transformers & Convolutions in Modern Image Deep Networks | Recent success of vision transformers has inspired a series of vision backbones with novel feature transformation paradigms, which report steady performance gain. Although the novel feature transformation designs are often claimed as the source of gain, some backbones may benefit from advanced engineering techniques, w... | ['Xiaowei Hu', 'Yu Qiao', 'Xiaogang Wang', 'Jie zhou', 'Lewei Lu', 'Xizhou Zhu', 'Wenhai Wang', 'Linjie Xing', 'Sitong Wu', 'Weiyun Wang', 'Min Shi', 'Jifeng Dai'] | 2022-11-10 | null | null | null | null | ['spatial-token-mixer', 'image-deep-networks'] | ['computer-vision', 'computer-vision'] | [ 2.10637897e-01 -2.70486295e-01 1.03051983e-01 -3.22298586e-01
-3.28042716e-01 -5.75904608e-01 8.72942805e-01 -2.22954571e-01
-2.81081915e-01 3.20471525e-01 3.04018319e-01 -3.98862660e-01
-1.64688349e-01 -7.29176700e-01 -6.98630750e-01 -8.95505309e-01
8.32545832e-02 -3.69799793e-01 4.63045895e-01 -4.68957454... | [9.62692928314209, 1.911351203918457] |
1d3ea688-da20-4b58-b1ca-473dfd12819c | localizing-anomalies-from-weakly-labeled | 2008.08944 | null | https://arxiv.org/abs/2008.08944v3 | https://arxiv.org/pdf/2008.08944v3.pdf | Localizing Anomalies from Weakly-Labeled Videos | Video anomaly detection under video-level labels is currently a challenging task. Previous works have made progresses on discriminating whether a video sequencecontains anomalies. However, most of them fail to accurately localize the anomalous events within videos in the temporal domain. In this paper, we propose a Wea... | ['Jian Yang', 'Zhen Cui', 'Chuanwei Zhou', 'Hui Lv', 'Chunyan Xu'] | 2020-08-20 | null | null | null | null | ['anomaly-detection-in-surveillance-videos', 'anomaly-detection-in-surveillance-videos'] | ['computer-vision', 'methodology'] | [ 1.86207473e-01 -6.20573223e-01 -5.10441028e-02 -2.77658194e-01
-4.67869252e-01 -3.57969463e-01 5.32856703e-01 3.41397494e-01
-2.12554991e-01 3.35335016e-01 1.21718921e-01 -9.50459242e-02
-1.03835367e-01 -4.30970281e-01 -4.23352867e-01 -9.22174394e-01
-3.84777933e-01 -3.10812593e-01 4.77873713e-01 -4.51504625... | [7.847970485687256, 1.5991747379302979] |
ed2ac614-bd81-48ad-83f7-66aa7ab44839 | hie-sql-history-information-enhanced-network-1 | 2203.07376 | null | https://arxiv.org/abs/2203.07376v2 | https://arxiv.org/pdf/2203.07376v2.pdf | HIE-SQL: History Information Enhanced Network for Context-Dependent Text-to-SQL Semantic Parsing | Recently, context-dependent text-to-SQL semantic parsing which translates natural language into SQL in an interaction process has attracted a lot of attention. Previous works leverage context-dependence information either from interaction history utterances or the previous predicted SQL queries but fail in taking advan... | ['Changshan Li', 'Xingjun Wang', 'Baohua Dong', 'Haibin Wang', 'Yanzhao Zheng'] | 2022-03-14 | null | https://aclanthology.org/2022.findings-acl.236 | https://aclanthology.org/2022.findings-acl.236.pdf | findings-acl-2022-5 | ['text-to-sql'] | ['computer-code'] | [ 1.77746624e-01 3.20740521e-01 -4.55445021e-01 -1.13521218e+00
-9.86994624e-01 -7.15292573e-01 6.52918935e-01 2.86091417e-01
-1.23611003e-01 2.77276933e-01 5.31165540e-01 -5.72950959e-01
2.13525981e-01 -1.11837435e+00 -1.05984330e+00 4.10199791e-01
3.50736856e-01 6.21498823e-01 6.11831129e-01 -3.97759944... | [9.933039665222168, 7.8452067375183105] |
20abdfb6-f0fc-4bcd-a95b-8d6b379ff6b2 | detecting-affordances-by-visuomotor | 1611.00274 | null | http://arxiv.org/abs/1611.00274v1 | http://arxiv.org/pdf/1611.00274v1.pdf | Detecting Affordances by Visuomotor Simulation | The term "affordance" denotes the behavioral meaning of objects. We propose a
cognitive architecture for the detection of affordances in the visual modality.
This model is based on the internal simulation of movement sequences. For each
movement step, the resulting sensory state is predicted by a forward model,
which i... | ['Ralf Möller', 'Hendrik Hasenbein', 'Wolfram Schenck'] | 2016-11-01 | null | null | null | null | ['affordance-detection'] | ['computer-vision'] | [ 3.31809193e-01 1.32725820e-01 1.12998724e-01 -1.21117815e-01
8.01584423e-02 -5.79870343e-01 7.89945781e-01 -2.82984767e-02
-5.40568054e-01 5.91139257e-01 -1.34445488e-01 -2.33179882e-01
-2.23629639e-01 -7.11498976e-01 -6.18141115e-01 -5.80082655e-01
-3.40294361e-01 4.32983458e-01 2.61555880e-01 -4.57172304... | [4.6127238273620605, 0.8330073356628418] |
3032d484-b3e1-4d46-9d20-f5a04b41d638 | team-donotdistribute-at-semeval-2020-task-11 | 2008.09703 | null | https://arxiv.org/abs/2008.09703v1 | https://arxiv.org/pdf/2008.09703v1.pdf | Team DoNotDistribute at SemEval-2020 Task 11: Features, Finetuning, and Data Augmentation in Neural Models for Propaganda Detection in News Articles | This paper presents our systems for SemEval 2020 Shared Task 11: Detection of Propaganda Techniques in News Articles. We participate in both the span identification and technique classification subtasks and report on experiments using different BERT-based models along with handcrafted features. Our models perform well ... | ['Nazli Goharian', 'Michael Kranzlein', 'Shabnam Behzad'] | 2020-08-21 | null | https://aclanthology.org/2020.semeval-1.196 | https://aclanthology.org/2020.semeval-1.196.pdf | semeval-2020 | ['propaganda-detection'] | ['natural-language-processing'] | [-2.02454068e-02 -2.41049558e-01 -5.07772624e-01 -1.80617988e-01
-1.02249336e+00 -7.87398696e-01 1.51618087e+00 3.55710268e-01
-7.85414457e-01 3.80277485e-01 1.04785478e+00 -8.90620708e-01
3.81227210e-02 -3.58340442e-01 -2.26811022e-01 -2.29085296e-01
-2.93956935e-01 3.28617766e-02 1.95148274e-01 -4.63474125... | [8.469738006591797, 10.676770210266113] |
90cdf3ac-8a35-47a0-83b0-1cef9961382b | extractive-summarization-of-call-transcripts | 2103.10599 | null | https://arxiv.org/abs/2103.10599v2 | https://arxiv.org/pdf/2103.10599v2.pdf | Extractive Summarization of Call Transcripts | Text summarization is the process of extracting the most important information from the text and presenting it concisely in fewer sentences. Call transcript is a text that involves textual description of a phone conversation between a customer (caller) and agent(s) (customer representatives). This paper presents an ind... | ['Aleksandr Iakubovich', 'Pratik K. Biswas'] | 2021-03-19 | null | null | null | null | ['punctuation-restoration'] | ['natural-language-processing'] | [ 6.05450749e-01 4.33193535e-01 1.15262382e-01 -3.54531884e-01
-1.20224094e+00 -6.87427640e-01 8.05682302e-01 7.57338047e-01
7.94202611e-02 1.17609799e+00 1.39289832e+00 -7.55688474e-02
1.40477344e-01 -1.76273406e-01 4.67211902e-02 -2.64154017e-01
9.48441401e-02 7.15983510e-01 -1.84373274e-01 -3.39489758... | [12.52334213256836, 9.509374618530273] |
476449e1-af6d-4b91-a6dd-a636b52ae8dc | whose-hand-is-this-person-identification-from | 2011.08900 | null | https://arxiv.org/abs/2011.08900v1 | https://arxiv.org/pdf/2011.08900v1.pdf | Whose hand is this? Person Identification from Egocentric Hand Gestures | Recognizing people by faces and other biometrics has been extensively studied in computer vision. But these techniques do not work for identifying the wearer of an egocentric (first-person) camera because that person rarely (if ever) appears in their own first-person view. But while one's own face is not frequently vis... | ['David Crandall', 'Yanwei Fu', 'Satoshi Tsutsui'] | 2020-11-17 | null | null | null | null | ['person-identification'] | ['computer-vision'] | [ 1.22158058e-01 -2.48139009e-01 -1.22429766e-01 -3.15956771e-01
-1.98268201e-02 -9.78665173e-01 5.36367953e-01 -8.65947247e-01
-4.29430991e-01 3.35704744e-01 7.62130171e-02 3.46671529e-02
9.07092392e-02 -3.59466195e-01 -3.88844997e-01 -9.02308762e-01
8.33021328e-02 2.79880017e-01 -1.58441558e-01 6.08358979... | [6.661126136779785, -0.6248324513435364] |
a308808c-dba9-49dc-9586-bbfa3e7ac3d6 | a-4d-light-field-dataset-and-cnn | 1608.06985 | null | http://arxiv.org/abs/1608.06985v1 | http://arxiv.org/pdf/1608.06985v1.pdf | A 4D Light-Field Dataset and CNN Architectures for Material Recognition | We introduce a new light-field dataset of materials, and take advantage of
the recent success of deep learning to perform material recognition on the 4D
light-field. Our dataset contains 12 material categories, each with 100 images
taken with a Lytro Illum, from which we extract about 30,000 patches in total.
To the be... | ['Jun-Yan Zhu', 'Ting-Chun Wang', 'Ravi Ramamoorthi', 'Ebi Hiroaki', 'Alexei A. Efros', 'Manmohan Chandraker'] | 2016-08-24 | null | null | null | null | ['material-recognition'] | ['computer-vision'] | [ 6.72832310e-01 -3.71644229e-01 9.23411250e-02 -3.86655897e-01
-4.65190858e-01 -5.40656149e-01 5.78517258e-01 -3.33542943e-01
-3.96696515e-02 5.22227943e-01 -6.60938248e-02 -2.30400205e-01
2.03146204e-01 -9.31875706e-01 -1.00083041e+00 -6.71652615e-01
3.94253463e-01 2.34635040e-01 3.71519566e-01 1.40589401... | [9.627568244934082, -2.7028849124908447] |
a59fb0b0-3b7a-43bf-8b8a-7b5a115eebba | patch-drosonet-classifying-image-partitions | 2305.05256 | null | https://arxiv.org/abs/2305.05256v1 | https://arxiv.org/pdf/2305.05256v1.pdf | Patch-DrosoNet: Classifying Image Partitions With Fly-Inspired Models For Lightweight Visual Place Recognition | Visual place recognition (VPR) enables autonomous systems to localize themselves within an environment using image information. While Convolution Neural Networks (CNNs) currently dominate state-of-the-art VPR performance, their high computational requirements make them unsuitable for platforms with budget or size const... | ['Shoaib Ehsan', 'Klaus D. McDonald-Maier', 'Michael Milford', 'Bruno Ferrarini', 'Bruno Arcanjo'] | 2023-05-09 | null | null | null | null | ['visual-place-recognition'] | ['computer-vision'] | [ 1.41302601e-01 -1.23858124e-01 -2.09913164e-01 -3.61397803e-01
-1.17612079e-01 -6.57759964e-01 7.05060601e-01 1.33875817e-01
-6.71285868e-01 4.63288605e-01 -6.01886213e-02 -3.96758288e-01
2.23366126e-01 -8.97138298e-01 -7.76885390e-01 -4.93559927e-01
-1.36265783e-02 1.11551262e-01 6.44099951e-01 -2.23452851... | [7.683662414550781, -1.828377604484558] |
913e5cd7-a5fa-43d0-93c3-8016dec7e9bf | courier-contrastive-user-intention | 2306.05001 | null | https://arxiv.org/abs/2306.05001v1 | https://arxiv.org/pdf/2306.05001v1.pdf | COURIER: Contrastive User Intention Reconstruction for Large-Scale Pre-Train of Image Features | With the development of the multi-media internet, visual characteristics have become an important factor affecting user interests. Thus, incorporating visual features is a promising direction for further performance improvements in click-through rate (CTR) prediction. However, we found that simply injecting the image e... | ['Yang Yang', 'Xiaoyi Zeng', 'Qingwen Liu', 'De-Chuan Zhan', 'Ju Huang', 'OU Dan', 'Chenglei Dai', 'Jia-Qi Yang'] | 2023-06-08 | null | null | null | null | ['click-through-rate-prediction'] | ['miscellaneous'] | [-1.12768911e-01 -2.81187981e-01 -5.93156517e-01 -5.81948102e-01
-5.24371386e-01 -3.72150689e-01 4.93063778e-01 1.14268787e-01
-5.54948866e-01 3.43628585e-01 4.79558498e-01 -3.59117180e-01
-8.52321163e-02 -9.79069710e-01 -8.01678598e-01 -2.40831807e-01
-2.10419208e-01 -1.73258577e-02 2.78228581e-01 -1.43852830... | [10.145482063293457, 5.444944858551025] |
5099bc1b-9c53-4615-8975-70524593dc61 | polarized-color-image-denoising-using | 2207.00215 | null | https://arxiv.org/abs/2207.00215v2 | https://arxiv.org/pdf/2207.00215v2.pdf | Polarized Color Image Denoising using Pocoformer | Polarized color photography provides both visual textures and object surficial information in one single snapshot. However, the use of the directional polarizing filter array causes extremely lower photon count and SNR compared to conventional color imaging. Thus, the feature essentially leads to unpleasant noisy image... | ['Yinqiang Zheng', 'Haiyang Jiang', 'Zhuoxiao Li'] | 2022-07-01 | null | null | null | null | ['color-image-denoising'] | ['computer-vision'] | [ 4.45997983e-01 -3.33095819e-01 4.42737520e-01 -1.90311503e-02
-6.98289871e-01 -5.86111188e-01 3.25523674e-01 -3.63584310e-01
-1.58437207e-01 8.40008616e-01 -1.41809076e-01 -1.57835037e-01
-1.88175455e-01 -6.29289567e-01 -5.75876892e-01 -1.50016391e+00
3.22958946e-01 -2.56314516e-01 6.53107390e-02 -6.83326623... | [10.572628021240234, -2.607869863510132] |
502cfec5-1bd3-4901-97c9-d0b25a8a21e4 | consistency-aware-shading-orders-selective | 1810.09706 | null | http://arxiv.org/abs/1810.09706v1 | http://arxiv.org/pdf/1810.09706v1.pdf | Consistency-aware Shading Orders Selective Fusion for Intrinsic Image Decomposition | We address the problem of decomposing a single image into reflectance and
shading. The difficulty comes from the fact that the components of image---the
surface albedo, the direct illumination, and the ambient illumination---are
coupled heavily in observed image. We propose to infer the shading by ordering
pixels by th... | ['Nanning Zheng', 'Badong Chen', 'Zejian yuan', 'Ang Li', 'Yuanliu Liu'] | 2018-10-23 | null | null | null | null | ['intrinsic-image-decomposition'] | ['computer-vision'] | [ 4.81478155e-01 -4.90786105e-01 4.63578999e-01 -7.77151287e-01
-6.32511556e-01 -5.14058888e-01 3.21759433e-01 -3.90488774e-01
-7.52537996e-02 5.93762696e-01 4.36249375e-02 1.20668985e-01
-8.22218508e-03 -6.59244001e-01 -4.85591888e-01 -1.28439236e+00
2.94459730e-01 2.29837537e-01 4.78487313e-01 -2.63776660... | [9.92122745513916, -2.972916603088379] |
5c4077d7-fa9c-46dd-9c13-ff71b90135db | detecting-adversarial-faces-using-only-real | 2304.11359 | null | https://arxiv.org/abs/2304.11359v2 | https://arxiv.org/pdf/2304.11359v2.pdf | Detecting Adversarial Faces Using Only Real Face Self-Perturbations | Adversarial attacks aim to disturb the functionality of a target system by adding specific noise to the input samples, bringing potential threats to security and robustness when applied to facial recognition systems. Although existing defense techniques achieve high accuracy in detecting some specific adversarial faces... | ['Ning Yu', 'Jiazhong Chen', 'Ping Li', 'Xiaorui Lin', 'Jinyuan Zhang', 'Hefei Ling', 'Yongqin Xian', 'Qian Wang'] | 2023-04-22 | null | null | null | null | ['face-detection'] | ['computer-vision'] | [ 3.65978718e-01 2.32195899e-01 3.49340200e-01 -3.12424451e-01
-4.90368426e-01 -9.91387725e-01 5.87551296e-01 -5.42675912e-01
-9.91199352e-03 5.30677795e-01 -3.67878318e-01 -2.23946780e-01
2.50231624e-01 -1.00382924e+00 -7.44344413e-01 -8.42105567e-01
-2.28321329e-01 2.74613708e-01 1.64328024e-01 -5.30297518... | [12.792119979858398, 1.0910850763320923] |
46add195-5e30-4ba1-ba24-b404307a475c | simf-semantics-aware-interactive-motion | 2306.14941 | null | https://arxiv.org/abs/2306.14941v1 | https://arxiv.org/pdf/2306.14941v1.pdf | SIMF: Semantics-aware Interactive Motion Forecasting for Autonomous Driving | Autonomous vehicles require motion forecasting of their surrounding multi-agents (pedestrians and vehicles) to make optimal decisions for navigation. The existing methods focus on techniques to utilize the positions and velocities of these agents and fail to capture semantic information from the scene. Moreover, to mit... | ['Ahmed H. Qureshi', 'Vidyaa Krishnan Nivash'] | 2023-06-26 | null | null | null | null | ['motion-prediction', 'autonomous-vehicles', 'motion-forecasting'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-1.36922702e-01 -2.20641851e-01 -4.71067667e-01 -5.96874475e-01
-6.01367354e-01 -4.32801187e-01 9.50031281e-01 1.52088791e-01
-8.28830063e-01 6.69803679e-01 6.12981498e-01 -9.71195698e-02
9.24772173e-02 -1.00659740e+00 -6.37945354e-01 -7.88175344e-01
-2.45280415e-01 6.20592952e-01 7.53583014e-01 -1.27460450... | [5.94264554977417, 0.8018349409103394] |
fe6ad8d0-e92e-43c1-a931-760d3f4fc95f | to-pretrain-or-not-to-pretrain-a-case-study | 2307.03275 | null | https://arxiv.org/abs/2307.03275v1 | https://arxiv.org/pdf/2307.03275v1.pdf | To pretrain or not to pretrain? A case study of domain-specific pretraining for semantic segmentation in histopathology | Annotating medical imaging datasets is costly, so fine-tuning (or transfer learning) is the most effective method for digital pathology vision applications such as disease classification and semantic segmentation. However, due to texture bias in models trained on real-world images, transfer learning for histopathology ... | ['Shireen Elhabian', 'Beatrice Knudsen', 'Tushar Kataria'] | 2023-07-06 | null | null | null | null | ['cell-segmentation', 'transfer-learning'] | ['medical', 'miscellaneous'] | [ 3.41187000e-01 1.54618949e-01 -2.14965209e-01 -5.82723558e-01
-8.79967391e-01 -5.02052009e-01 3.44435215e-01 3.90414119e-01
-7.50301540e-01 6.74331129e-01 3.69520305e-04 -3.98224115e-01
-3.99064757e-02 -7.23142326e-01 -7.27676451e-01 -9.25138652e-01
1.98935136e-01 6.71426058e-01 2.86916196e-01 1.09865718... | [15.047077178955078, -2.6620988845825195] |
6f898422-8361-4225-95a3-dddd66832774 | clipcrop-conditioned-cropping-driven-by | 2211.11492 | null | https://arxiv.org/abs/2211.11492v1 | https://arxiv.org/pdf/2211.11492v1.pdf | ClipCrop: Conditioned Cropping Driven by Vision-Language Model | Image cropping has progressed tremendously under the data-driven paradigm. However, current approaches do not account for the intentions of the user, which is an issue especially when the composition of the input image is complex. Moreover, labeling of cropping data is costly and hence the amount of data is limited, le... | ['Imari Sato', 'Yoichi Sato', 'Stephen Lin', 'Han Hu', 'Ji Li', 'Yinqiang Zheng', 'Yuhui Yuan', 'Zhirong Wu', 'Mingxi Cheng', 'Zhihang Zhong'] | 2022-11-21 | null | null | null | null | ['image-cropping'] | ['computer-vision'] | [ 7.65494585e-01 -1.09723739e-01 -6.98734000e-02 -3.07202220e-01
-8.45902920e-01 -6.58183277e-01 5.50851226e-01 -2.59716541e-01
-2.00595677e-01 1.63540885e-01 2.71357954e-01 -2.50573277e-01
5.49778223e-01 -7.15412736e-01 -1.10972285e+00 -3.28340471e-01
6.85277522e-01 1.29772112e-01 9.55263823e-02 -3.97568405... | [11.244884490966797, -0.07275836914777756] |
8194e975-e1c4-404b-a649-3c9f1d94c93e | mosi-multimodal-corpus-of-sentiment-intensity | 1606.06259 | null | http://arxiv.org/abs/1606.06259v2 | http://arxiv.org/pdf/1606.06259v2.pdf | MOSI: Multimodal Corpus of Sentiment Intensity and Subjectivity Analysis in Online Opinion Videos | People are sharing their opinions, stories and reviews through online video
sharing websites every day. Studying sentiment and subjectivity in these
opinion videos is experiencing a growing attention from academia and industry.
While sentiment analysis has been successful for text, it is an understudied
research questi... | ['Louis-Philippe Morency', 'Eli Pincus', 'Rowan Zellers', 'Amir Zadeh'] | 2016-06-20 | null | null | null | null | ['subjectivity-analysis'] | ['natural-language-processing'] | [ 1.69557482e-01 -3.28185171e-01 -4.33746934e-01 -7.41636395e-01
-1.00191331e+00 -8.64790559e-01 6.09420002e-01 1.45983025e-01
-5.36514342e-01 3.99768263e-01 8.08128178e-01 2.64017552e-01
3.26375037e-01 2.96711270e-02 -4.52313393e-01 -6.93236530e-01
1.15522794e-01 -3.12163174e-01 2.79310904e-02 -2.21755430... | [13.064153671264648, 5.238861083984375] |
6be2dfdb-c698-4b23-ad67-9423fa0cc59b | selective-pre-training-for-private-fine | 2305.13865 | null | https://arxiv.org/abs/2305.13865v1 | https://arxiv.org/pdf/2305.13865v1.pdf | Selective Pre-training for Private Fine-tuning | Suppose we want to train text prediction models in email clients or word processors. The models must preserve the privacy of user data and adhere to a specific fixed size to meet memory and inference time requirements. We introduce a generic framework to solve this problem. Specifically, we are given a public dataset $... | ['Huishuai Zhang', 'Jian Yin', 'Tomasz Lukasz Religa', 'Saurabh Naik', 'Zinan Lin', 'Janardhan Kulkarni', 'Sivakanth Gopi', 'Da Yu'] | 2023-05-23 | null | null | null | null | ['model-compression'] | ['methodology'] | [ 3.05223405e-01 3.98440987e-01 -3.15583855e-01 -6.66463375e-01
-1.36139679e+00 -6.66736007e-01 1.01148985e-01 2.55981740e-03
-6.02400362e-01 7.15266287e-01 -3.39932084e-01 -6.45366609e-01
-2.92380482e-01 -1.07816303e+00 -1.14945257e+00 -8.12363148e-01
-6.63667172e-02 7.95766175e-01 -2.70392627e-01 1.85289949... | [5.90421724319458, 6.842350959777832] |
6e07dfd3-546a-4c88-88f2-90d1d3b08e2e | semeval-2021-task-1-lexical-complexity | 2106.00473 | null | https://arxiv.org/abs/2106.00473v1 | https://arxiv.org/pdf/2106.00473v1.pdf | SemEval-2021 Task 1: Lexical Complexity Prediction | This paper presents the results and main findings of SemEval-2021 Task 1 - Lexical Complexity Prediction. We provided participants with an augmented version of the CompLex Corpus (Shardlow et al 2020). CompLex is an English multi-domain corpus in which words and multi-word expressions (MWEs) were annotated with respect... | ['Marcos Zampieri', 'Gustavo Henrique Paetzold', 'Richard Evans', 'Matthew Shardlow'] | 2021-06-01 | null | https://aclanthology.org/2021.semeval-1.1 | https://aclanthology.org/2021.semeval-1.1.pdf | semeval-2021 | ['lexical-complexity-prediction'] | ['natural-language-processing'] | [-4.46789622e-01 1.17737278e-01 -6.90044016e-02 -2.75707126e-01
-8.97121012e-01 -7.34851301e-01 3.24604839e-01 5.36917984e-01
-1.03934360e+00 8.59424353e-01 5.44377029e-01 -1.98672086e-01
1.79822743e-01 -4.30384785e-01 -2.17329130e-01 3.48939270e-01
1.00369053e-02 5.31977057e-01 1.17009908e-01 -5.32850802... | [10.645464897155762, 10.480134963989258] |
2d08f446-4baf-4104-880b-c4480cc07740 | interactive-mobile-app-navigation-with | 2202.02312 | null | https://arxiv.org/abs/2202.02312v3 | https://arxiv.org/pdf/2202.02312v3.pdf | A Dataset for Interactive Vision-Language Navigation with Unknown Command Feasibility | Vision-language navigation (VLN), in which an agent follows language instruction in a visual environment, has been studied under the premise that the input command is fully feasible in the environment. Yet in practice, a request may not be possible due to language ambiguity or environment changes. To study VLN with unk... | ['Bryan A. Plummer', 'Kate Saenko', 'Ranjitha Kumar', 'Sanjna Agrawal', 'Deniz Arsan', 'Andrea Burns'] | 2022-02-04 | null | null | null | null | ['vision-language-navigation'] | ['computer-vision'] | [ 4.05291229e-01 -1.45656794e-01 -3.38202924e-01 -4.46964115e-01
-1.00967705e+00 -9.09385204e-01 3.72666836e-01 -2.48746514e-01
-5.93806088e-01 6.82922721e-01 2.33614355e-01 -8.90622675e-01
-5.75881964e-03 -3.11254412e-01 -9.30760562e-01 4.21992270e-03
6.00704700e-02 4.88420337e-01 3.52673560e-01 -3.03426057... | [4.451298713684082, 0.7181888222694397] |
c2d93b05-a70b-41d0-b3a5-0b78076a6c91 | domain-adaptation-for-head-pose-estimation | null | null | https://ieeexplore.ieee.org/document/10021684 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10021684 | Domain Adaptation for Head Pose Estimation Using Relative Pose Consistency | Head pose estimation plays a vital role in biometric systems related to facial and human behavior analysis. Typically, neural networks are trained on head pose datasets. Unfortunately, manual or sensor-based annotation of head pose is impractical. A solution is synthetic training data generated from 3D face models, whi... | ['Jörn Ostermann', 'Felix Kuhnke'] | 2023-01-19 | null | null | null | ieee-transactions-on-biometrics-behavior-and-2 | ['head-pose-estimation'] | ['computer-vision'] | [ 2.31772438e-01 3.59843582e-01 -1.70288429e-01 -9.10823107e-01
-7.19322085e-01 -4.30997401e-01 4.51826334e-01 -1.73855156e-01
-5.63490570e-01 7.49837458e-01 -4.97167872e-04 1.68538243e-01
1.24884814e-01 -4.73532975e-01 -8.85000110e-01 -6.23264194e-01
4.53536697e-02 6.45137489e-01 -8.23071748e-02 2.98786163... | [13.41518497467041, 0.1368931084871292] |
99506bc4-93c2-4e8c-bad8-9d94b92895cd | an-inter-lingual-reference-approach-for-multi | 1309.6650 | null | http://arxiv.org/abs/1309.6650v1 | http://arxiv.org/pdf/1309.6650v1.pdf | An Inter-lingual Reference Approach For Multi-Lingual Ontology Matching | Ontologies are considered as the backbone of the Semantic Web. With the
rising success of the Semantic Web, the number of participating communities
from different countries is constantly increasing. The growing number of
ontologies available in different natural languages leads to an
interoperability problem. In this p... | ['Haytham Al-Feel', 'Ralph Schafermeier', 'Adrian Paschke'] | 2013-09-25 | null | null | null | null | ['ontology-matching'] | ['knowledge-base'] | [-5.40371597e-01 2.07182735e-01 -6.77351877e-02 -1.46540642e-01
-2.97979653e-01 -6.10616803e-01 4.94483471e-01 5.06024063e-01
-4.24831390e-01 7.90327549e-01 2.74252981e-01 -3.24817419e-01
-5.35905063e-01 -9.77674544e-01 -1.07081339e-01 5.83667494e-02
1.89478114e-01 8.11475694e-01 5.21812379e-01 -9.50086355... | [9.182230949401855, 8.135478019714355] |
4d0a23eb-c9f6-430a-880d-6ef76106eaad | teddi-sample-text-data-diversity-sample-for | null | null | https://aclanthology.org/2022.lrec-1.123 | https://aclanthology.org/2022.lrec-1.123.pdf | TeDDi Sample: Text Data Diversity Sample for Language Comparison and Multilingual NLP | We present the TeDDi sample, a diversity sample of text data for language comparison and multilingual Natural Language Processing. The TeDDi sample currently features 89 languages based on the typological diversity sample in the World Atlas of Language Structures. It consists of more than 20k texts and is accompanied b... | ['Tanja Samardzic', 'Olga Pelloni', 'Ximena Gutierrez-Vasques', 'Christian Bentz', 'Steven Moran'] | null | null | null | null | lrec-2022-6 | ['multilingual-nlp'] | ['natural-language-processing'] | [-5.89001298e-01 -3.77546356e-04 -6.11163676e-01 -1.63008764e-01
-9.46200728e-01 -8.11123013e-01 1.01485252e+00 6.76728487e-01
-9.84207273e-01 7.97594488e-01 1.16910803e+00 -3.95764381e-01
-1.99516192e-01 -3.89266223e-01 2.41724011e-02 3.66402492e-02
-1.28732964e-01 9.13601220e-01 -2.02168822e-01 -5.78544557... | [10.384483337402344, 10.09496784210205] |
ad76465f-b4cf-4dc7-9794-b6f20fbf62c5 | 3d-reconstruction-of-spherical-images-based | 2306.12770 | null | https://arxiv.org/abs/2306.12770v2 | https://arxiv.org/pdf/2306.12770v2.pdf | 3D Reconstruction of Spherical Images based on Incremental Structure from Motion | 3D reconstruction plays an increasingly important role in modern photogrammetric systems. Conventional satellite or aerial-based remote sensing (RS) platforms can provide the necessary data sources for the 3D reconstruction of large-scale landforms and cities. Even with low-altitude UAVs (Unmanned Aerial Vehicles), 3D ... | ['Wu Chen', 'Duojie Weng', 'Yaxin Li', 'Kan You', 'San Jiang'] | 2023-06-22 | null | null | null | null | ['3d-reconstruction'] | ['computer-vision'] | [ 1.64654821e-01 -2.46941790e-01 5.83927095e-01 -3.29457313e-01
-3.95730436e-01 -7.09451675e-01 6.28849924e-01 -1.45522699e-01
-4.29076821e-01 4.42245543e-01 -3.22161674e-01 -3.64539295e-01
-2.39750490e-01 -9.06785429e-01 -5.44899881e-01 -6.62520826e-01
1.17853530e-01 6.77788973e-01 4.39980388e-01 -3.84656310... | [8.380221366882324, -2.556986093521118] |
7a9ceef4-e56c-43cc-ab05-91a04531bdf7 | text2seg-remote-sensing-image-semantic | 2304.10597 | null | https://arxiv.org/abs/2304.10597v1 | https://arxiv.org/pdf/2304.10597v1.pdf | Text2Seg: Remote Sensing Image Semantic Segmentation via Text-Guided Visual Foundation Models | Recent advancements in foundation models (FMs), such as GPT-4 and LLaMA, have attracted significant attention due to their exceptional performance in zero-shot learning scenarios. Similarly, in the field of visual learning, models like Grounding DINO and the Segment Anything Model (SAM) have exhibited remarkable progre... | ['Sheng Li', 'Mengxuan Hu', 'Lan Mu', 'Gengchen Mai', 'Zhongliang Zhou', 'Jielu Zhang'] | 2023-04-20 | null | null | null | null | ['segmentation-of-remote-sensing-imagery'] | ['miscellaneous'] | [ 5.74524701e-01 -5.98662943e-02 -2.02618569e-01 -2.79132158e-01
-9.33271527e-01 -7.22707033e-01 5.75938225e-01 1.74532071e-01
-2.10211843e-01 4.04815555e-01 -6.73588142e-02 -7.69858360e-01
-1.06558956e-01 -8.05731535e-01 -5.37290990e-01 -4.88148421e-01
-1.51274770e-01 3.96551132e-01 3.22923928e-01 -4.16202992... | [9.474609375, -1.2804348468780518] |
4cafa948-5d59-40ed-bbcc-f3b364679953 | graph-convolutional-auto-encoder-with-bi | 2003.04508 | null | https://arxiv.org/abs/2003.04508v3 | https://arxiv.org/pdf/2003.04508v3.pdf | Unsupervised Graph Embedding via Adaptive Graph Learning | Graph autoencoders (GAEs) are powerful tools in representation learning for graph embedding. However, the performance of GAEs is very dependent on the quality of the graph structure, i.e., of the adjacency matrix. In other words, GAEs would perform poorly when the adjacency matrix is incomplete or be disturbed. In this... | ['Xuelong. Li', 'Yunxing Zhang', 'Rui Zhang'] | 2020-03-10 | null | null | null | null | ['graph-reconstruction'] | ['graphs'] | [-2.91865081e-01 2.87368298e-01 -2.29440734e-01 2.66829114e-02
1.66792914e-01 -4.32250053e-01 1.98612884e-01 1.33295015e-01
-5.18439859e-02 2.30069786e-01 1.73959494e-01 -2.84569442e-01
-3.07677805e-01 -1.10839510e+00 -5.69179416e-01 -1.03322780e+00
-9.92183480e-03 2.65090108e-01 -9.63540524e-02 -3.78093988... | [7.22027063369751, 6.255685806274414] |
10deef70-61a0-4864-bcb9-6b501b429fc5 | audio-visual-scene-analysis-with-self | 1804.03641 | null | http://arxiv.org/abs/1804.03641v2 | http://arxiv.org/pdf/1804.03641v2.pdf | Audio-Visual Scene Analysis with Self-Supervised Multisensory Features | The thud of a bouncing ball, the onset of speech as lips open -- when visual
and audio events occur together, it suggests that there might be a common,
underlying event that produced both signals. In this paper, we argue that the
visual and audio components of a video signal should be modeled jointly using a
fused mult... | ['Alexei A. Efros', 'Andrew Owens'] | 2018-04-10 | audio-visual-scene-analysis-with-self-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Andrew_Owens_Audio-Visual_Scene_Analysis_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Andrew_Owens_Audio-Visual_Scene_Analysis_ECCV_2018_paper.pdf | eccv-2018-9 | ['audio-source-separation'] | ['audio'] | [ 3.93197328e-01 -3.99510264e-01 -1.00933000e-01 -5.87593997e-03
-1.20704544e+00 -7.98436761e-01 4.54587162e-01 6.88393191e-02
8.75679627e-02 4.00116652e-01 4.91683066e-01 -1.67818084e-01
9.56452861e-02 2.93088481e-02 -9.49650347e-01 -7.15943873e-01
8.58677477e-02 -4.09050375e-01 1.62281454e-01 2.39032865... | [14.602190971374512, 5.0448455810546875] |
fc232446-33a1-4787-838f-c6856656c3a9 | multiple-imputation-with-neural-network | 2211.13297 | null | https://arxiv.org/abs/2211.13297v2 | https://arxiv.org/pdf/2211.13297v2.pdf | Multiple Imputation with Neural Network Gaussian Process for High-dimensional Incomplete Data | Missing data are ubiquitous in real world applications and, if not adequately handled, may lead to the loss of information and biased findings in downstream analysis. Particularly, high-dimensional incomplete data with a moderate sample size, such as analysis of multi-omics data, present daunting challenges. Imputation... | ['Qi Long', 'Zhiqi Bu', 'Zongyu Dai'] | 2022-11-23 | null | null | null | null | ['matrix-completion'] | ['methodology'] | [ 4.29373264e-01 -2.40611345e-01 -4.19875860e-01 -4.13806856e-01
-9.95182216e-01 -2.08558768e-01 4.11060423e-01 1.91872176e-02
-2.19746381e-01 1.38217247e+00 3.88469189e-01 -3.68821114e-01
-5.45709014e-01 -8.41705263e-01 -9.74566519e-01 -8.18090916e-01
2.18433127e-01 5.91979504e-01 -6.40773475e-01 3.41755480... | [7.607182502746582, 4.724435806274414] |
83783181-95c0-4b25-a1f6-12e4fafc07fd | deep-learning-based-defect-classification-and-1 | 2211.02185 | null | https://arxiv.org/abs/2211.02185v1 | https://arxiv.org/pdf/2211.02185v1.pdf | Deep Learning based Defect classification and detection in SEM images: A Mask R-CNN approach | In this research work, we have demonstrated the application of Mask-RCNN (Regional Convolutional Neural Network), a deep-learning algorithm for computer vision and specifically object detection, to semiconductor defect inspection domain. Stochastic defect detection and classification during semiconductor manufacturing ... | ['Magdy A. Bayoumi', 'Philippe Leray', 'Sandip Halder', 'Kasem Khalil', 'Enrique Dehaerne', 'Bappaditya Dey'] | 2022-11-03 | null | null | null | null | ['defect-detection'] | ['computer-vision'] | [ 4.59908307e-01 1.78784236e-01 4.52054292e-01 -4.86408770e-02
-4.82389331e-01 -4.23499823e-01 1.46767631e-01 7.23680496e-01
-1.90796509e-01 4.82788891e-01 -6.84933424e-01 -4.95091856e-01
-2.81729519e-01 -9.49879408e-01 -3.68947774e-01 -9.06742752e-01
2.78021127e-01 9.50069308e-01 3.07434499e-01 -2.31519446... | [7.314776420593262, 1.9213964939117432] |
aae26acd-2882-49e8-a457-c9285da7988f | building-state-of-the-art-distant-speech | 1803.10109 | null | http://arxiv.org/abs/1803.10109v1 | http://arxiv.org/pdf/1803.10109v1.pdf | Building state-of-the-art distant speech recognition using the CHiME-4 challenge with a setup of speech enhancement baseline | This paper describes a new baseline system for automatic speech recognition
(ASR) in the CHiME-4 challenge to promote the development of noisy ASR in
speech processing communities by providing 1) state-of-the-art system with a
simplified single system comparable to the complicated top systems in the
challenge, 2) publi... | ['Szu-Jui Chen', 'Aswin Shanmugam Subramanian', 'Hainan Xu', 'Shinji Watanabe'] | 2018-03-27 | null | null | null | null | ['noisy-speech-recognition', 'distant-speech-recognition'] | ['speech', 'speech'] | [ 2.37485953e-02 -1.65229991e-01 6.89534366e-01 -2.63175189e-01
-1.40962338e+00 -8.63576531e-02 1.97851017e-01 -2.97213584e-01
-8.54436755e-01 3.71584833e-01 6.29428267e-01 -6.18731201e-01
-1.34064317e-01 -5.19961156e-02 -4.51505333e-01 -7.42099106e-01
-1.53729677e-01 -1.06727391e-01 -4.00374085e-02 -4.84294593... | [14.823627471923828, 6.042372226715088] |
10c76181-12d1-49b2-a634-8aa4d1daa5a4 | boundary-detection-with-bert-for-span-level | null | null | https://aclanthology.org/2021.findings-acl.60 | https://aclanthology.org/2021.findings-acl.60.pdf | Boundary Detection with BERT for Span-level Emotion Cause Analysis | null | ['Daling Wang', 'Yifei Zhang', 'Shi Feng', 'Wei Gao', 'Xiangju Li'] | null | null | null | null | findings-acl-2021-8 | ['boundary-detection'] | ['computer-vision'] | [-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... | [-7.540960788726807, 3.5181708335876465] |
efddb8ce-59d4-4ba3-be69-4f6a8964aa62 | breaking-the-object-in-video-object | 2212.06200 | null | https://arxiv.org/abs/2212.06200v2 | https://arxiv.org/pdf/2212.06200v2.pdf | Breaking the "Object" in Video Object Segmentation | The appearance of an object can be fleeting when it transforms. As eggs are broken or paper is torn, their color, shape and texture can change dramatically, preserving virtually nothing of the original except for the identity itself. Yet, this important phenomenon is largely absent from existing video object segmentati... | ['Adrien Gaidon', 'Jie Li', 'Pavel Tokmakov'] | 2022-12-12 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Tokmakov_Breaking_the_Object_in_Video_Object_Segmentation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Tokmakov_Breaking_the_Object_in_Video_Object_Segmentation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-object-segmentation'] | ['computer-vision'] | [ 4.39251810e-01 -2.68442839e-01 -2.52768755e-01 -1.87011942e-01
-4.04736936e-01 -1.02998209e+00 6.24508679e-01 -1.94152579e-01
-2.59526074e-01 5.27947187e-01 -7.83470869e-02 6.46719411e-02
2.06769541e-01 -3.39114338e-01 -9.85143185e-01 -6.78661823e-01
5.30046560e-02 3.78682703e-01 6.32409930e-01 -1.82545260... | [9.171525001525879, -0.21019823849201202] |
ad81fb24-3d93-4743-a79a-65f94b7bdc23 | sample-and-predict-your-latent-modality-free | 2305.15924 | null | https://arxiv.org/abs/2305.15924v1 | https://arxiv.org/pdf/2305.15924v1.pdf | Sample and Predict Your Latent: Modality-free Sequential Disentanglement via Contrastive Estimation | Unsupervised disentanglement is a long-standing challenge in representation learning. Recently, self-supervised techniques achieved impressive results in the sequential setting, where data is time-dependent. However, the latter methods employ modality-based data augmentations and random sampling or solve auxiliary task... | ['Omri Azencot', 'Nimrod Berman', 'Ilan Naiman'] | 2023-05-25 | null | null | null | null | ['disentanglement'] | ['methodology'] | [ 2.71263838e-01 -1.86437070e-01 -3.88821959e-01 -2.38288686e-01
-1.21660495e+00 -6.18435681e-01 9.26171243e-01 -1.34088933e-01
-2.29096696e-01 6.27811491e-01 4.81514722e-01 -3.38071994e-02
8.14569890e-02 -3.44071716e-01 -5.66097617e-01 -7.53505945e-01
1.42304897e-01 5.82265198e-01 -3.32821429e-01 1.15693636... | [10.699224472045898, 0.3126383423805237] |
e8dc52e8-a717-4b20-a71e-6c6b528c047d | a-geometric-analysis-of-deep-generative-image | null | null | https://openreview.net/forum?id=GH7QRzUDdXG | https://openreview.net/pdf?id=GH7QRzUDdXG | A Geometric Analysis of Deep Generative Image Models and Its Applications | Generative adversarial networks have emerged as a powerful unsupervised method to model the statistical patterns of real-world data sets, such as natural images. These networks are trained to map random inputs in their latent space to new samples representative of the learned data. However, the structure of the latent ... | ['Carlos R Ponce', 'Binxu Wang'] | 2021-01-01 | null | null | null | iclr-2021-1 | ['image-variation'] | ['computer-vision'] | [ 4.66536731e-01 4.01199609e-01 7.33197667e-03 -2.35974893e-01
-3.01571518e-01 -1.14360607e+00 9.37323451e-01 -7.00220764e-01
1.67210191e-01 3.50690424e-01 4.87446219e-01 -1.63109377e-01
-1.58212453e-01 -8.33198249e-01 -8.59401882e-01 -1.03629506e+00
1.25555366e-01 6.68995380e-01 -3.98698717e-01 -2.61280358... | [11.618754386901855, -0.1257167011499405] |
c901c5c1-de37-4d5b-a649-d762d04451c7 | comparing-approaches-to-dravidian-language | 2103.05552 | null | https://arxiv.org/abs/2103.05552v1 | https://arxiv.org/pdf/2103.05552v1.pdf | Comparing Approaches to Dravidian Language Identification | This paper describes the submissions by team HWR to the Dravidian Language Identification (DLI) shared task organized at VarDial 2021 workshop. The DLI training set includes 16,674 YouTube comments written in Roman script containing code-mixed text with English and one of the three South Dravidian languages: Kannada, M... | ['Marcos Zampieri', 'Tharindu Ranasinghe', 'Tommi Jauhiainen'] | 2021-03-09 | null | https://aclanthology.org/2021.vardial-1.14 | https://aclanthology.org/2021.vardial-1.14.pdf | eacl-vardial-2021-4 | ['dialect-identification'] | ['natural-language-processing'] | [-3.39379102e-01 -1.06320642e-01 -4.45359154e-03 -2.38923103e-01
-7.89216399e-01 -6.89358234e-01 9.56335187e-01 2.16510162e-01
-9.33677495e-01 4.72584546e-01 3.60270411e-01 -6.87084854e-01
-1.71878673e-02 -2.34186351e-01 -1.41168058e-01 -3.25101495e-01
1.31949365e-01 1.03250909e+00 9.75338966e-02 -3.71414542... | [10.22842025756836, 10.643775939941406] |
bbd6f4c5-7433-4633-b6e1-7a32d76a0b70 | on-a-notion-of-independence-proposed-by-teddy | 2102.10342 | null | https://arxiv.org/abs/2102.10342v1 | https://arxiv.org/pdf/2102.10342v1.pdf | On a notion of independence proposed by Teddy Seidenfeld | Teddy Seidenfeld has been arguing for quite a long time that binary preference models are not powerful enough to deal with a number of crucial aspects of imprecision and indeterminacy in uncertain inference and decision making. It is at his insistence that we initiated our study of so-called sets of desirable option se... | ['Gert de Cooman', 'Jasper De Bock'] | 2021-02-20 | null | null | null | null | ['decision-making-under-uncertainty', 'decision-making-under-uncertainty'] | ['medical', 'reasoning'] | [ 4.84242067e-02 5.28302670e-01 -1.27093539e-01 -8.54859829e-01
-3.81607771e-01 -9.20388520e-01 8.51888061e-01 2.05268607e-01
-6.36255085e-01 9.43894148e-01 3.63708973e-01 -1.10119319e+00
-1.08613133e+00 -6.88555658e-01 -2.43388951e-01 -5.30311704e-01
-3.75114754e-02 6.43967807e-01 -1.26317829e-01 -2.30163991... | [8.19632625579834, 5.233717441558838] |
21bd29b2-cb4a-42bc-9254-eabadf67c7d3 | nsurl-2019-shared-task-8-semantic-question | 1909.09691 | null | https://arxiv.org/abs/1909.09691v1 | https://arxiv.org/pdf/1909.09691v1.pdf | NSURL-2019 Shared Task 8: Semantic Question Similarity in Arabic | Question semantic similarity (Q2Q) is a challenging task that is very useful in many NLP applications, such as detecting duplicate questions and question answering systems. In this paper, we present the results and findings of the shared task (Semantic Question Similarity in Arabic). The task was organized as part of t... | ['Hussein T. Al-Natsheh', 'Hesham Al-Bataineh', 'Wael Farhan', 'Ahmad Mustafa', 'Haitham Seelawi'] | 2019-09-12 | null | null | null | null | ['question-similarity'] | ['natural-language-processing'] | [ 2.20325403e-02 1.83370844e-01 4.68736231e-01 -6.02675200e-01
-8.71433139e-01 -9.92926002e-01 3.98025125e-01 7.81595170e-01
-4.64358419e-01 5.31685352e-01 4.48054314e-01 -2.79234499e-01
-1.99493676e-01 -4.64796454e-01 -4.21378374e-01 1.36480018e-01
3.37901175e-01 7.28516281e-01 6.85634136e-01 -1.03474176... | [11.366737365722656, 8.050127983093262] |
c1908367-23b9-48fc-ad00-bea1806f589c | fair-decision-making-under-uncertainty | 2301.12364 | null | https://arxiv.org/abs/2301.12364v1 | https://arxiv.org/pdf/2301.12364v1.pdf | Fair Decision-making Under Uncertainty | There has been concern within the artificial intelligence (AI) community and the broader society regarding the potential lack of fairness of AI-based decision-making systems. Surprisingly, there is little work quantifying and guaranteeing fairness in the presence of uncertainty which is prevalent in many socially sensi... | ['Jeremy C. Weiss', 'Wenbin Zhang'] | 2023-01-29 | null | null | null | null | ['decision-making-under-uncertainty', 'marketing', 'decision-making-under-uncertainty'] | ['medical', 'miscellaneous', 'reasoning'] | [ 4.29070950e-01 5.04093111e-01 -5.71971953e-01 -9.17366385e-01
-2.13660270e-01 -5.70634246e-01 5.25114596e-01 3.47089410e-01
-4.15353388e-01 1.20639598e+00 6.77484497e-02 -3.95217985e-01
-6.38282597e-01 -6.72452629e-01 -3.26053858e-01 -4.92143840e-01
5.28209880e-02 2.72014767e-01 -4.93255377e-01 1.27817001... | [8.820112228393555, 5.332359313964844] |
eca022a2-f884-4044-833b-765bb27ed226 | learning-noise-invariant-representations-for-1 | null | null | https://openreview.net/forum?id=ryz4mqPx9m | https://openreview.net/pdf?id=ryz4mqPx9m | Learning Noise-Invariant Representations for Robust Speech Recognition | Despite rapid advances in speech recognition, current models remain brittle to superficial perturbations to their inputs. Small amounts of noise can destroy the performance of an otherwise state-of-the-art model. To harden models against background noise, practitioners often perform data augmentation, adding artificial... | ['Anonymous'] | 2018-10-02 | null | null | null | null | ['robust-speech-recognition'] | ['speech'] | [ 7.46208668e-01 3.08175474e-01 1.15649112e-01 -4.20971185e-01
-1.12390065e+00 -6.58017337e-01 6.47673726e-01 7.98896477e-02
-7.24242389e-01 7.63303518e-01 3.68348986e-01 -2.52817988e-01
1.27622426e-01 -4.29102540e-01 -9.13945198e-01 -7.55093873e-01
2.00774167e-02 3.08357954e-01 2.27159187e-01 -2.42589116... | [10.590235710144043, 8.090202331542969] |
9dfb539e-6137-4c6c-b63f-9638e9ebfe47 | template-based-named-entity-recognition-using | 2106.01760 | null | https://arxiv.org/abs/2106.01760v1 | https://arxiv.org/pdf/2106.01760v1.pdf | Template-Based Named Entity Recognition Using BART | There is a recent interest in investigating few-shot NER, where the low-resource target domain has different label sets compared with a resource-rich source domain. Existing methods use a similarity-based metric. However, they cannot make full use of knowledge transfer in NER model parameters. To address the issue, we ... | ['Yue Zhang', 'Sen yang', 'Jian Liu', 'Yu Wu', 'Leyang Cui'] | 2021-06-03 | null | https://aclanthology.org/2021.findings-acl.161 | https://aclanthology.org/2021.findings-acl.161.pdf | findings-acl-2021-8 | ['few-shot-ner'] | ['natural-language-processing'] | [ 1.26541659e-01 -3.27720523e-01 -1.01241998e-01 -4.37525243e-01
-1.19918609e+00 -8.34562957e-01 3.66308808e-01 -7.81174973e-02
-1.05167937e+00 8.64546418e-01 3.45999062e-01 -7.38045126e-02
2.60876745e-01 -5.95937312e-01 -4.11139578e-01 -3.57322752e-01
4.83294666e-01 3.24992031e-01 4.48293746e-01 -3.72516751... | [9.802560806274414, 9.368378639221191] |
6219ecbb-513b-44ac-af8c-9468a8d1e24d | real-time-webcam-heart-rate-and-variability | 2012.15846 | null | https://arxiv.org/abs/2012.15846v1 | https://arxiv.org/pdf/2012.15846v1.pdf | Real-time Webcam Heart-Rate and Variability Estimation with Clean Ground Truth for Evaluation | Remote photo-plethysmography (rPPG) uses a camera to estimate a person's heart rate (HR). Similar to how heart rate can provide useful information about a person's vital signs, insights about the underlying physio/psychological conditions can be obtained from heart rate variability (HRV). HRV is a measure of the fine f... | ['Jan van Gemert', 'Marian Bittner', 'Amogh Gudi'] | 2020-12-31 | null | null | null | null | ['heart-rate-variability'] | ['medical'] | [ 2.06664011e-01 -2.55514145e-01 1.01723485e-02 -3.05602580e-01
-5.24858952e-01 -5.12226224e-01 5.15185259e-02 3.42542864e-02
-8.11068714e-02 7.02387929e-01 4.22741771e-01 5.18533707e-01
1.20327428e-01 -8.69661033e-01 1.36917874e-01 -5.96613526e-01
-3.03913146e-01 1.03737861e-01 -2.09795609e-01 5.08033745... | [13.907084465026855, 2.8632638454437256] |
a979fb68-eb20-456a-8897-fcc28e88f507 | connectivity-optimized-nested-graph-networks | 2302.14102 | null | https://arxiv.org/abs/2302.14102v1 | https://arxiv.org/pdf/2302.14102v1.pdf | Connectivity Optimized Nested Graph Networks for Crystal Structures | Graph neural networks (GNNs) have been applied to a large variety of applications in materials science and chemistry. Here, we recapitulate the graph construction for crystalline (periodic) materials and investigate its impact on the GNNs model performance. We suggest the asymmetric unit cell as a representation to red... | ['Pascal Friederich', 'Jan Stühmer', 'Patrick Reiser', 'Robin Ruff'] | 2023-02-27 | null | null | null | null | ['graph-construction'] | ['graphs'] | [ 5.02349377e-01 3.65921229e-01 -3.27445835e-01 1.31851003e-01
2.61438370e-01 -1.26146808e-01 6.97067201e-01 1.51985958e-01
-7.74707720e-02 7.73785174e-01 8.02076161e-02 -7.46331215e-01
-1.95636258e-01 -1.27636015e+00 -8.56824577e-01 -9.76486087e-01
-3.04145604e-01 6.49494946e-01 3.90274793e-01 -5.18641174... | [6.447460651397705, 5.976089000701904] |
abc57a6e-91c0-4602-b768-c367e3a00a1e | an-fpga-based-on-device-reinforcement | 2005.04646 | null | https://arxiv.org/abs/2005.04646v4 | https://arxiv.org/pdf/2005.04646v4.pdf | An FPGA-Based On-Device Reinforcement Learning Approach using Online Sequential Learning | DQN (Deep Q-Network) is a method to perform Q-learning for reinforcement learning using deep neural networks. DQNs require a large buffer and batch processing for an experience replay and rely on a backpropagation based iterative optimization, making them difficult to be implemented on resource-limited edge devices. In... | ['Mineto Tsukada', 'Hiroki Matsutani', 'Hirohisa Watanabe'] | 2020-05-10 | null | null | null | null | ['l2-regularization'] | ['methodology'] | [-3.79574895e-01 -1.93553850e-01 -1.21430464e-01 -2.58552909e-01
1.46250561e-01 2.88743735e-03 -3.20634395e-02 7.43149519e-02
-8.69112849e-01 6.88449740e-01 -8.08610618e-01 -5.17666280e-01
-1.18972778e-01 -8.88305366e-01 -7.83342659e-01 -5.23130953e-01
2.63050832e-02 1.66663826e-01 2.62205482e-01 -5.56216896... | [8.23653793334961, 2.6699788570404053] |
dad4a5c1-db89-4f2f-9cac-8d5408fa04d9 | invariant-teacher-and-equivariant-student-for | 2012.09398 | null | https://arxiv.org/abs/2012.09398v1 | https://arxiv.org/pdf/2012.09398v1.pdf | Invariant Teacher and Equivariant Student for Unsupervised 3D Human Pose Estimation | We propose a novel method based on teacher-student learning framework for 3D human pose estimation without any 3D annotation or side information. To solve this unsupervised-learning problem, the teacher network adopts pose-dictionary-based modeling for regularization to estimate a physically plausible 3D pose. To handl... | ['Ya zhang', 'Maosen Li', 'Siheng Chen', 'Chenxin Xu'] | 2020-12-17 | null | null | null | null | ['unsupervised-3d-human-pose-estimation'] | ['computer-vision'] | [-5.37591994e-01 3.92268419e-01 -1.70972437e-01 -4.88151938e-01
-5.88487625e-01 -2.10332885e-01 2.25427717e-01 -3.46573800e-01
-3.84836525e-01 4.44961846e-01 2.14111850e-01 -1.22120120e-01
-3.86982858e-02 -4.96647924e-01 -9.11905468e-01 -5.18626213e-01
4.38507879e-03 8.58958423e-01 1.91160753e-01 -2.45297194... | [7.003758430480957, -0.9443991184234619] |
e506ff1f-a7c4-4b92-940b-a6fe264d65ee | combining-manual-and-automatic-prosodic | null | null | https://aclanthology.org/L16-1613 | https://aclanthology.org/L16-1613.pdf | Combining Manual and Automatic Prosodic Annotation for Expressive Speech Synthesis | Text-to-speech has long been centered on the production of an intelligible message of good quality. More recently, interest has shifted to the generation of more natural and expressive speech. A major issue of existing approaches is that they usually rely on a manual annotation in expressive styles, which tends to be r... | ['Thomas Fran{\\c{c}}ois', 'rine', 'S Brognaux', 'Marco Saerens'] | 2016-05-01 | combining-manual-and-automatic-prosodic-1 | https://aclanthology.org/L16-1613 | https://aclanthology.org/L16-1613.pdf | lrec-2016-5 | ['expressive-speech-synthesis'] | ['speech'] | [ 4.83440101e-01 4.90496933e-01 6.26024231e-02 -5.97001910e-01
-9.92006481e-01 -5.18334270e-01 4.85755771e-01 2.21459553e-01
-2.97017485e-01 7.25014389e-01 5.58137596e-01 -8.55578557e-02
1.41478226e-01 -4.09152567e-01 -3.13335419e-01 -7.97644079e-01
5.82070708e-01 5.55455267e-01 2.34878272e-01 -2.65177876... | [14.679389953613281, 6.573779582977295] |
196b1176-bc74-4501-a321-56462cc72f3e | a-multimodal-transformer-fusing-clinical | 2208.10240 | null | https://arxiv.org/abs/2208.10240v2 | https://arxiv.org/pdf/2208.10240v2.pdf | A Multimodal Transformer: Fusing Clinical Notes with Structured EHR Data for Interpretable In-Hospital Mortality Prediction | Deep-learning-based clinical decision support using structured electronic health records (EHR) has been an active research area for predicting risks of mortality and diseases. Meanwhile, large amounts of narrative clinical notes provide complementary information, but are often not integrated into predictive models. In ... | ['Chao Chen', 'Fusheng Wang', 'Kayley Abell-Hart', 'Songzhu Zheng', 'Rachel Wong', 'Xinyu Dong', 'Weimin Lyu'] | 2022-08-09 | null | null | null | null | ['mortality-prediction'] | ['medical'] | [-2.28298046e-02 8.97701979e-02 -3.06599468e-01 -5.81747115e-01
-9.94803488e-01 -3.81739467e-01 -1.48452455e-02 8.24036717e-01
-2.61816710e-01 8.07927549e-01 1.08549237e+00 -3.48626196e-01
-5.88173985e-01 -5.49252808e-01 -9.83020067e-02 -6.02190256e-01
-4.39365417e-01 5.28949380e-01 -5.42308927e-01 1.65890619... | [7.933475017547607, 6.462647438049316] |
94fdef1e-8576-4332-ab95-88bb22f78a6b | socratic-pretraining-question-driven | 2212.10449 | null | https://arxiv.org/abs/2212.10449v3 | https://arxiv.org/pdf/2212.10449v3.pdf | Socratic Pretraining: Question-Driven Pretraining for Controllable Summarization | In long document controllable summarization, where labeled data is scarce, pretrained models struggle to adapt to the task and effectively respond to user queries. In this paper, we introduce Socratic pretraining, a question-driven, unsupervised pretraining objective specifically designed to improve controllability in ... | ['Chien-Sheng Wu', 'Wojciech Kryściński', 'Alexander R. Fabbri', 'Artidoro Pagnoni'] | 2022-12-20 | null | null | null | null | ['question-generation'] | ['natural-language-processing'] | [ 6.02984369e-01 5.81994414e-01 -3.25224310e-01 -3.58045965e-01
-1.53714824e+00 -8.79614830e-01 7.10124314e-01 3.82645011e-01
-4.29401904e-01 9.23223019e-01 7.84049392e-01 -1.23679280e-01
-6.73760325e-02 -4.98724014e-01 -5.81664979e-01 1.73151344e-01
4.33527023e-01 1.09326720e+00 2.16664955e-01 -6.55218720... | [12.214491844177246, 9.063788414001465] |
43098628-f6e6-49ad-8dc7-275f21a406f3 | analysis-of-task-transferability-in-large-pre | 2307.00823 | null | https://arxiv.org/abs/2307.00823v1 | https://arxiv.org/pdf/2307.00823v1.pdf | Analysis of Task Transferability in Large Pre-trained Classifiers | Transfer learning transfers the knowledge acquired by a model from a source task to multiple downstream target tasks with minimal fine-tuning. The success of transfer learning at improving performance, especially with the use of large pre-trained models has made transfer learning an essential tool in the machine learni... | ['Jihun Hamm', 'Yunbei Zhang', 'Akshay Mehra'] | 2023-07-03 | null | null | null | null | ['transfer-learning'] | ['miscellaneous'] | [ 4.96040821e-01 7.84044564e-02 3.25930342e-02 -4.72415954e-01
-7.72929788e-01 -6.91953659e-01 6.17266119e-01 1.59243017e-01
-5.90636432e-01 6.12728179e-01 -2.96897255e-03 -1.72998384e-01
-3.22173834e-01 -6.03181481e-01 -9.53637600e-01 -8.16488504e-01
4.40087616e-02 3.39855611e-01 2.63870656e-01 -1.30727232... | [9.774170875549316, 3.240222930908203] |
94c4c9f6-e77a-449e-beb1-08bb065dde1e | effect-of-personalized-calibration-on-gaze | 2109.12801 | null | https://arxiv.org/abs/2109.12801v1 | https://arxiv.org/pdf/2109.12801v1.pdf | Effect Of Personalized Calibration On Gaze Estimation Using Deep-Learning | With the increase in computation power and the development of new state-of-the-art deep learning algorithms, appearance-based gaze estimation is becoming more and more popular. It is believed to work well with curated laboratory data sets, however it faces several challenges when deployed in real world scenario. One su... | ['Didier Schwab', 'Sébastien Riou', 'Nairit Bandyopadhyay'] | 2021-09-27 | null | null | null | null | ['gaze-estimation'] | ['computer-vision'] | [-9.25377533e-02 9.78191644e-02 4.46707457e-01 -5.40917277e-01
6.04073480e-02 -3.37212145e-01 3.27143371e-01 -1.83882162e-01
-7.63279974e-01 6.48883522e-01 -2.05607340e-01 5.04235663e-02
-2.53714994e-02 -2.50710666e-01 -8.16630781e-01 -5.95842600e-01
9.75652039e-02 3.33124429e-01 1.04702435e-01 -1.13745928... | [14.143427848815918, 0.07841881364583969] |
112a90e4-a4ee-4b65-a85a-6efab341e3c8 | automated-concatenation-of-embeddings-for-1 | 2010.05006 | null | https://arxiv.org/abs/2010.05006v4 | https://arxiv.org/pdf/2010.05006v4.pdf | Automated Concatenation of Embeddings for Structured Prediction | Pretrained contextualized embeddings are powerful word representations for structured prediction tasks. Recent work found that better word representations can be obtained by concatenating different types of embeddings. However, the selection of embeddings to form the best concatenated representation usually varies depe... | ['Kewei Tu', 'Fei Huang', 'Zhongqiang Huang', 'Tao Wang', 'Nguyen Bach', 'Yong Jiang', 'Xinyu Wang'] | 2020-10-10 | automated-concatenation-of-embeddings-for | https://aclanthology.org/2021.acl-long.206 | https://aclanthology.org/2021.acl-long.206.pdf | acl-2021-5 | ['aspect-extraction', 'semantic-dependency-parsing'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.28220657e-01 -2.19585910e-01 -4.32683259e-01 -2.35592037e-01
-6.37412071e-01 -2.81903297e-01 5.37208378e-01 3.75881493e-01
-8.42397749e-01 4.45492625e-01 5.92245698e-01 -2.03638926e-01
-1.54253393e-01 -5.33103108e-01 -4.58865434e-01 -6.55508935e-01
9.62222144e-02 6.65198267e-01 2.04761386e-01 3.46160159... | [10.582197189331055, 8.602304458618164] |
89d53a06-921e-459b-ada9-1964587e25b4 | npms-neural-parametric-models-for-3d | 2104.00702 | null | https://arxiv.org/abs/2104.00702v2 | https://arxiv.org/pdf/2104.00702v2.pdf | NPMs: Neural Parametric Models for 3D Deformable Shapes | Parametric 3D models have enabled a wide variety of tasks in computer graphics and vision, such as modeling human bodies, faces, and hands. However, the construction of these parametric models is often tedious, as it requires heavy manual tweaking, and they struggle to represent additional complexity and details such a... | ['Angela Dai', 'Matthias Nießner', 'Justus Thies', 'Aljaž Božič', 'Pablo Palafox'] | 2021-04-01 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Palafox_NPMs_Neural_Parametric_Models_for_3D_Deformable_Shapes_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Palafox_NPMs_Neural_Parametric_Models_for_3D_Deformable_Shapes_ICCV_2021_paper.pdf | iccv-2021-1 | ['pose-transfer'] | ['computer-vision'] | [ 6.03902191e-02 -1.08468145e-01 -2.92080529e-02 -2.90422767e-01
-4.21251893e-01 -7.99642265e-01 6.49683058e-01 -3.57207179e-01
-9.07364935e-02 5.65137804e-01 1.21643081e-01 -2.44710110e-02
-4.46182955e-03 -3.82432997e-01 -1.03834224e+00 -6.48376405e-01
8.92597884e-02 6.66523933e-01 9.35187414e-02 5.15122600... | [6.997763156890869, -1.296563982963562] |
f93623da-98cb-4c7c-b243-b316ac5bcab5 | machine-learning-approaches-to-predict-breast | 2206.14972 | null | https://arxiv.org/abs/2206.14972v1 | https://arxiv.org/pdf/2206.14972v1.pdf | Machine Learning Approaches to Predict Breast Cancer: Bangladesh Perspective | Nowadays, Breast cancer has risen to become one of the most prominent causes of death in recent years. Among all malignancies, this is the most frequent and the major cause of death for women globally. Manually diagnosing this disease requires a good amount of time and expertise. Breast cancer detection is time-consumi... | ['Md Jihadul Islam', 'Flora Akter', 'Choyon Chandra Bonik', 'Nazmul Islam Khan', 'Arindom Kundu', 'Taminul Islam'] | 2022-06-30 | null | null | null | null | ['breast-cancer-detection', 'breast-cancer-detection'] | ['knowledge-base', 'medical'] | [-2.20795963e-02 1.11918807e-01 -7.93948591e-01 -6.10102117e-01
-4.72021669e-01 1.39074057e-01 4.56054449e-01 6.81305707e-01
-3.97178710e-01 1.03351068e+00 -1.44539595e-01 -6.04434431e-01
-5.50590992e-01 -1.07465565e+00 -6.20262511e-02 -7.24954963e-01
1.05414338e-01 9.29858863e-01 1.52374178e-01 6.58866018... | [8.434679985046387, 4.852565288543701] |
c38a5b74-1086-4c0c-bc32-eb04a57eca91 | learning-neuro-symbolic-programs-for-language | 2211.06652 | null | https://arxiv.org/abs/2211.06652v2 | https://arxiv.org/pdf/2211.06652v2.pdf | Learning Neuro-symbolic Programs for Language Guided Robot Manipulation | Given a natural language instruction and an input scene, our goal is to train a model to output a manipulation program that can be executed by the robot. Prior approaches for this task possess one of the following limitations: (i) rely on hand-coded symbols for concepts limiting generalization beyond those seen during ... | ['Rohan Paul', 'Parag Singla', 'Rahul Jain', 'Vishwajeet Agrawal', 'Arnav Tuli', 'Vishal Bindal', 'Himanshu Singh', 'Namasivayam Kalithasan'] | 2022-11-12 | null | null | null | null | ['robot-manipulation'] | ['robots'] | [ 4.51397270e-01 2.81182855e-01 -2.64215916e-01 -4.22951400e-01
-2.16144994e-01 -6.57595158e-01 6.54059589e-01 3.00909672e-02
-3.53124201e-01 2.90968627e-01 1.12041183e-01 -4.83494610e-01
-5.81768109e-03 -6.72699511e-01 -1.16293132e+00 -4.57076669e-01
-2.36368671e-01 3.62947494e-01 1.53293177e-01 -3.35147351... | [4.504756927490234, 0.7058550119400024] |
86d42ac7-7654-44b2-b339-2d8387179b41 | cardiac-arrhythmia-detection-from-ecg-with | 2010.03204 | null | https://arxiv.org/abs/2010.03204v1 | https://arxiv.org/pdf/2010.03204v1.pdf | Cardiac Arrhythmia Detection from ECG with Convolutional Recurrent Neural Networks | Except for a few specific types, cardiac arrhythmias are not immediately life-threatening. However, if not treated appropriately, they can cause serious complications. In particular, atrial fibrillation, which is characterized by fast and irregular heart beats, increases the risk of stroke. We propose three neural netw... | ['Damien Ferrario Mathieu Lemay', 'Ricard Delgado-Gonzalo', 'Jérôme Van Zaen'] | 2020-10-07 | null | null | null | null | ['arrhythmia-detection'] | ['medical'] | [ 2.37858444e-01 -2.65440196e-01 -3.95747870e-02 -1.99130297e-01
-7.66600728e-01 -5.40803611e-01 -3.36930454e-02 4.53600198e-01
-5.16493976e-01 8.63089502e-01 8.95368829e-02 -5.15028894e-01
-2.41049677e-01 -5.35904586e-01 -2.87681043e-01 -4.08606321e-01
-6.46312058e-01 1.15794368e-01 -2.65171260e-01 1.51239797... | [14.334147453308105, 3.3007349967956543] |
d0d2c206-2b3b-47a2-9fb9-e65427383d5f | invisible-backdoor-attacks-using-data | 2207.04209 | null | https://arxiv.org/abs/2207.04209v1 | https://arxiv.org/pdf/2207.04209v1.pdf | Invisible Backdoor Attacks Using Data Poisoning in the Frequency Domain | With the broad application of deep neural networks (DNNs), backdoor attacks have gradually attracted attention. Backdoor attacks are insidious, and poisoned models perform well on benign samples and are only triggered when given specific inputs, which cause the neural network to produce incorrect outputs. The state-of-... | ['Kai Chen', 'Ruigang Liang', 'Peizhuo Lv', 'Chang Yue'] | 2022-07-09 | null | null | null | null | ['data-poisoning'] | ['adversarial'] | [ 8.96340311e-02 -1.77319348e-01 -2.53685508e-02 -1.08150221e-01
-1.86958134e-01 -1.07116425e+00 6.06240094e-01 -7.98142478e-02
-5.35454512e-01 4.40290004e-01 -5.98406315e-01 -4.49492782e-01
2.15296805e-01 -1.03196979e+00 -8.27308118e-01 -1.00431538e+00
1.11173607e-01 1.20920807e-01 3.67282212e-01 -1.85801819... | [5.7022576332092285, 7.765721321105957] |
3e725718-1ba0-44a6-a433-27f4c98ef693 | adversarial-robustness-certification-for | 2306.13614 | null | https://arxiv.org/abs/2306.13614v1 | https://arxiv.org/pdf/2306.13614v1.pdf | Adversarial Robustness Certification for Bayesian Neural Networks | We study the problem of certifying the robustness of Bayesian neural networks (BNNs) to adversarial input perturbations. Given a compact set of input points $T \subseteq \mathbb{R}^m$ and a set of output points $S \subseteq \mathbb{R}^n$, we define two notions of robustness for BNNs in an adversarial setting: probabili... | ['Marta Kwiatkowska', 'Luca Laurenti', 'Andrea Patane', 'Matthew Wicker'] | 2023-06-23 | null | null | null | null | ['adversarial-robustness', 'traffic-sign-recognition'] | ['adversarial', 'computer-vision'] | [ 3.43571991e-01 4.45448995e-01 8.19207653e-02 -3.12475085e-01
-8.88821423e-01 -6.65926695e-01 3.66286449e-02 -5.93485218e-03
-4.51662391e-01 9.48539257e-01 -7.14666188e-01 -5.60951650e-01
-7.50970721e-01 -1.01512587e+00 -1.31887650e+00 -1.04465604e+00
-3.54915589e-01 4.08743262e-01 3.20366830e-01 -6.26374558... | [5.604480743408203, 7.654757976531982] |
9756ced7-5ead-4d37-9a2f-b745855243fd | slue-phase-2-a-benchmark-suite-of-diverse | 2212.10525 | null | https://arxiv.org/abs/2212.10525v2 | https://arxiv.org/pdf/2212.10525v2.pdf | SLUE Phase-2: A Benchmark Suite of Diverse Spoken Language Understanding Tasks | Spoken language understanding (SLU) tasks have been studied for many decades in the speech research community, but have not received as much attention as lower-level tasks like speech and speaker recognition. In particular, there are not nearly as many SLU task benchmarks, and many of the existing ones use data that is... | ['Shinji Watanabe', 'Karen Livescu', 'Hung-Yi Lee', 'Wei-Lun Wu', 'Roshan Sharma', 'Felix Wu', 'Ankita Pasad', 'Chyi-Jiunn Lin', 'Siddhant Arora', 'Suwon Shon'] | 2022-12-20 | null | null | null | null | ['dialog-act-classification', 'spoken-language-understanding', 'speaker-recognition', 'spoken-language-understanding'] | ['natural-language-processing', 'natural-language-processing', 'speech', 'speech'] | [ 5.23816824e-01 3.61477047e-01 5.00952639e-03 -7.75655985e-01
-1.35108101e+00 -7.71594465e-01 6.71797752e-01 1.33369014e-01
-3.41840506e-01 5.01501977e-01 8.65776360e-01 -5.85080981e-01
3.05703878e-01 -1.46646664e-01 -4.99190897e-01 -1.90822616e-01
5.32496255e-03 5.85104108e-01 2.33760297e-01 -1.99559480... | [14.035834312438965, 6.972362518310547] |
e7075d2e-d238-492b-985c-160989b3abea | dureader-retrieval-a-large-scale-chinese | 2203.10232 | null | https://arxiv.org/abs/2203.10232v4 | https://arxiv.org/pdf/2203.10232v4.pdf | DuReader_retrieval: A Large-scale Chinese Benchmark for Passage Retrieval from Web Search Engine | In this paper, we present DuReader_retrieval, a large-scale Chinese dataset for passage retrieval. DuReader_retrieval contains more than 90K queries and over 8M unique passages from a commercial search engine. To alleviate the shortcomings of other datasets and ensure the quality of our benchmark, we (1) reduce the fal... | ['Haifeng Wang', 'Hua Wu', 'Jing Liu', 'Qiaoqiao She', 'Ying Chen', 'Yingqi Qu', 'Hongyu Li', 'Yifu Qiu'] | 2022-03-19 | null | null | null | null | ['passage-retrieval'] | ['natural-language-processing'] | [-4.90598261e-01 -6.58552170e-01 -2.73914456e-01 -1.06821083e-01
-1.89406359e+00 -1.06874013e+00 5.69228768e-01 9.60775912e-02
-4.85326350e-01 8.49928081e-01 3.60846907e-01 -3.66461184e-03
-6.52596727e-02 -4.75629568e-01 -6.23209655e-01 -2.95314103e-01
2.73037434e-01 7.15240657e-01 5.37081122e-01 -4.94535804... | [11.505043983459473, 7.861293315887451] |
271dfa23-bb8d-4aae-aac2-60394b8e8dcb | a-light-weight-contextual-spelling-correction | 2108.07493 | null | https://arxiv.org/abs/2108.07493v1 | https://arxiv.org/pdf/2108.07493v1.pdf | A Light-weight contextual spelling correction model for customizing transducer-based speech recognition systems | It's challenging to customize transducer-based automatic speech recognition (ASR) system with context information which is dynamic and unavailable during model training. In this work, we introduce a light-weight contextual spelling correction model to correct context-related recognition errors in transducer-based ASR s... | ['Jinyu Li', 'Sheng Zhao', 'Yanqing Liu', 'Xiaoqiang Wang'] | 2021-08-17 | null | null | null | null | ['spelling-correction'] | ['natural-language-processing'] | [ 6.37564480e-01 -1.35282815e-01 4.24939469e-02 -4.53035682e-01
-1.40978038e+00 -4.42813814e-01 2.06539750e-01 -5.38618714e-02
-6.50202453e-01 4.77190137e-01 4.47708964e-01 -1.11384082e+00
5.43863773e-01 -1.90913871e-01 -6.52679622e-01 -3.20987076e-01
4.01748031e-01 2.70378798e-01 3.58892262e-01 -5.75846553... | [14.333955764770508, 6.8020920753479] |
a6c98700-83b3-4b3f-ac74-35d71f6484bd | road-network-representation-learning-a-dual | 2304.07298 | null | https://arxiv.org/abs/2304.07298v1 | https://arxiv.org/pdf/2304.07298v1.pdf | Road Network Representation Learning: A Dual Graph based Approach | Road network is a critical infrastructure powering many applications including transportation, mobility and logistics in real life. To leverage the input of a road network across these different applications, it is necessary to learn the representations of the roads in the form of vectors, which is named \emph{road net... | ['Cheng Long', 'Liang Zhang'] | 2023-04-13 | null | null | null | null | ['hyperedge-classification', 'graph-reconstruction'] | ['graphs', 'graphs'] | [ 1.30024493e-01 5.29627740e-01 -5.31199455e-01 -5.04708111e-01
1.75916702e-01 -5.82813501e-01 7.06187308e-01 1.59935132e-01
1.00577883e-01 6.51141644e-01 4.43571627e-01 -6.85120106e-01
-6.70652270e-01 -1.71589112e+00 -8.82324576e-01 -4.93881047e-01
-3.12491655e-01 4.29680377e-01 1.79402992e-01 -3.66112947... | [6.481655120849609, 2.1111128330230713] |
14170a00-0302-4e0a-ba51-2dacec153ee0 | zero-shot-transfer-for-implicit-discourse-1 | null | null | https://aclanthology.org/W19-5927 | https://aclanthology.org/W19-5927.pdf | Zero-shot transfer for implicit discourse relation classification | Automatically classifying the relation between sentences in a discourse is a challenging task, in particular when there is no overt expression of the relation. It becomes even more challenging by the fact that annotated training data exists only for a small number of languages, such as English and Chinese. We present a... | ['Robert {\\"O}stling', 'Murathan Kurfal{\\i}'] | 2019-09-01 | null | null | null | ws-2019-9 | ['implicit-discourse-relation-classification'] | ['natural-language-processing'] | [ 3.55700813e-02 6.81402504e-01 -5.43074965e-01 -2.44222328e-01
-8.39799643e-01 -4.73185152e-01 9.48163211e-01 3.21123183e-01
-5.13021231e-01 1.24072862e+00 5.35913646e-01 -5.03379941e-01
3.07458609e-01 -7.83625424e-01 -3.61864567e-01 -2.98781127e-01
-6.10588454e-02 8.07726085e-01 5.18191040e-01 -7.95052826... | [10.840078353881836, 9.306209564208984] |
1dc35de0-0b79-4035-859a-cc4d6fae0c3d | image-super-resolution-with-non-local-sparse | null | null | http://openaccess.thecvf.com//content/CVPR2021/html/Mei_Image_Super-Resolution_With_Non-Local_Sparse_Attention_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Mei_Image_Super-Resolution_With_Non-Local_Sparse_Attention_CVPR_2021_paper.pdf | Image Super-Resolution With Non-Local Sparse Attention | Both non-local (NL) operation and sparse representation are crucial for Single Image Super-Resolution (SISR). In this paper, we investigate their combinations and propose a novel Non-Local Sparse Attention (NLSA) with dynamic sparse attention pattern. NLSA is designed to retain long-range modeling capability from N... | ['Yuqian Zhou', 'Yuchen Fan', 'Yiqun Mei'] | 2021-06-19 | null | null | null | cvpr-2021-1 | ['long-range-modeling'] | ['natural-language-processing'] | [ 5.68237752e-02 -5.96652515e-02 -3.11389655e-01 -1.19014271e-01
-1.05817163e+00 1.67872733e-03 1.83003515e-01 -5.50669208e-02
-1.45956621e-01 4.32834834e-01 6.67559445e-01 4.19685036e-01
-5.75690828e-02 -5.87318540e-01 -8.12591672e-01 -8.22236180e-01
-1.61590442e-01 7.92377964e-02 5.34257829e-01 -1.08797818... | [10.945137023925781, -1.8701817989349365] |
e55e2e09-90d5-4666-affd-9d1d84972642 | chinese-grammatical-error-detection-based-on | null | null | https://aclanthology.org/2020.nlptea-1.15 | https://aclanthology.org/2020.nlptea-1.15.pdf | Chinese Grammatical Error Detection Based on BERT Model | Automatic grammatical error correction is of great value in assisting second language writing. In 2020, the shared task for Chinese grammatical error diagnosis(CGED) was held in NLP-TEA. As the LDU team, we participated the competition and submitted the final results. Our work mainly focused on grammatical error detect... | ['Mofan Duan', 'Yong Cheng'] | null | null | null | null | aacl-nlp-tea-2020-12-1 | ['grammatical-error-detection'] | ['natural-language-processing'] | [-3.72281253e-01 3.28685284e-01 2.86884397e-01 -4.38370019e-01
-9.27284002e-01 -2.11263776e-01 -2.05742434e-01 5.60919940e-01
-6.38314903e-01 1.23255563e+00 1.44913629e-01 -3.86176944e-01
3.43352258e-01 -4.40671593e-01 -6.12937212e-01 1.01478338e-01
2.25270391e-01 2.77858019e-01 -3.40814441e-02 -2.56821632... | [11.054112434387207, 10.797268867492676] |
74509302-ec77-484b-9dee-8cf21a946571 | characterizing-out-of-distribution-error-via | 2305.15640 | null | https://arxiv.org/abs/2305.15640v2 | https://arxiv.org/pdf/2305.15640v2.pdf | Characterizing Out-of-Distribution Error via Optimal Transport | Out-of-distribution (OOD) data poses serious challenges in deployed machine learning models, so methods of predicting a model's performance on OOD data without labels are important for machine learning safety. While a number of methods have been proposed by prior work, they often underestimate the actual error, sometim... | ['Katia Sycara', 'Joseph Campbell', 'Simon Stepputtis', 'Soheil Kolouri', 'Zhenlin Wang', 'Ketong Chen', 'Andrew Shen', 'Runtian Zhai', 'Yilong Qin', 'Yuzhe Lu'] | 2023-05-25 | null | null | null | null | ['pseudo-label'] | ['miscellaneous'] | [ 8.29634964e-02 -5.74557632e-02 -4.80611831e-01 -5.58844924e-01
-1.01793873e+00 -4.98183101e-01 5.61642289e-01 4.41317379e-01
-1.86260983e-01 7.35208988e-01 -2.34333754e-01 -4.52895582e-01
-1.06870987e-01 -4.85592782e-01 -8.28140795e-01 -5.90184748e-01
-8.28547254e-02 6.11576080e-01 5.14885783e-01 2.82975316... | [9.010170936584473, 3.847087860107422] |
92e50c3f-4fcc-460d-9763-d98b94c1c905 | recurrent-neural-networks-to-correct | 1608.03440 | null | http://arxiv.org/abs/1608.03440v3 | http://arxiv.org/pdf/1608.03440v3.pdf | Recurrent Neural Networks to Correct Satellite Image Classification Maps | While initially devised for image categorization, convolutional neural
networks (CNNs) are being increasingly used for the pixelwise semantic labeling
of images. However, the proper nature of the most common CNN architectures
makes them good at recognizing but poor at localizing objects precisely. This
problem is magni... | ['Pierre Alliez', 'Emmanuel Maggiori', 'Yuliya Tarabalka', 'Guillaume Charpiat'] | 2016-08-11 | null | null | null | null | ['satellite-image-classification', 'image-categorization'] | ['computer-vision', 'computer-vision'] | [ 5.68050504e-01 1.68794498e-01 1.17105395e-01 -4.43018585e-01
-3.42742443e-01 -4.99924272e-01 5.29710591e-01 7.60144889e-02
-6.41249955e-01 3.65602046e-01 -3.72457206e-02 -3.10493916e-01
-3.03950280e-01 -8.63241017e-01 -5.85948050e-01 -7.16632545e-01
2.74582896e-02 6.50225282e-02 4.44511920e-02 -2.15371117... | [9.635834693908691, 0.5762585997581482] |
231c48db-d989-4f4f-bafc-89cbd784acdb | htnet-human-topology-aware-network-for-3d | 2302.09790 | null | https://arxiv.org/abs/2302.09790v1 | https://arxiv.org/pdf/2302.09790v1.pdf | HTNet: Human Topology Aware Network for 3D Human Pose Estimation | 3D human pose estimation errors would propagate along the human body topology and accumulate at the end joints of limbs. Inspired by the backtracking mechanism in automatic control systems, we design an Intra-Part Constraint module that utilizes the parent nodes as the reference to build topological constraints for end... | ['Miaoju Ban', 'Jianbing Wu', 'Wenhao Li', 'Runwei Ding', 'Hong Liu', 'Jialun Cai'] | 2023-02-20 | null | null | null | null | ['3d-human-pose-estimation'] | ['computer-vision'] | [-3.84950370e-01 4.58520323e-01 -3.09748828e-01 -2.40529776e-01
1.01856053e-01 -4.01077271e-02 2.26662338e-01 -4.36894506e-01
-1.82239369e-01 4.90685135e-01 3.99121821e-01 4.30244297e-01
8.45866799e-02 -6.01507187e-01 -8.47568214e-01 -1.47702590e-01
-3.03782016e-01 8.07880223e-01 6.22530699e-01 -3.63061905... | [7.099092960357666, -0.6394210457801819] |
f6e04f33-5ce3-45de-8720-aa2432cb38fc | efficient-informed-proposals-for-discrete | 2302.13929 | null | https://arxiv.org/abs/2302.13929v1 | https://arxiv.org/pdf/2302.13929v1.pdf | Efficient Informed Proposals for Discrete Distributions via Newton's Series Approximation | Gradients have been exploited in proposal distributions to accelerate the convergence of Markov chain Monte Carlo algorithms on discrete distributions. However, these methods require a natural differentiable extension of the target discrete distribution, which often does not exist or does not provide effective gradient... | ['Ruqi Zhang', 'Dongkuan Xu', 'Bowen Lei', 'Dongyao Zhu', 'Yue Xiang'] | 2023-02-27 | null | null | null | null | ['efficient-exploration', 'extractive-document-summarization'] | ['methodology', 'natural-language-processing'] | [ 5.76860718e-02 -1.64586172e-01 -3.13402772e-01 -2.42790967e-01
-1.18828642e+00 -7.29849815e-01 7.93713689e-01 2.84345061e-01
-5.94986320e-01 1.15331328e+00 1.95983276e-01 -4.45896804e-01
-2.19871715e-01 -7.41670430e-01 -6.87629282e-01 -9.39282656e-01
2.27398410e-01 1.13709533e+00 -3.90664861e-02 1.42503604... | [6.897722244262695, 4.030930519104004] |
fdda4a5e-10e5-498a-9e5a-7b58d1cd9db6 | blindly-assess-image-quality-in-the-wild | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Su_Blindly_Assess_Image_Quality_in_the_Wild_Guided_by_a_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Su_Blindly_Assess_Image_Quality_in_the_Wild_Guided_by_a_CVPR_2020_paper.pdf | Blindly Assess Image Quality in the Wild Guided by a Self-Adaptive Hyper Network | Blind image quality assessment (BIQA) for authentically distorted images has always been a challenging problem, since images captured in the wild include varies contents and diverse types of distortions. The vast majority of prior BIQA methods focus on how to predict synthetic image quality, but fail when applied to re... | [' Yanning Zhang', ' Jinqiu Sun', ' Xin Ge', ' Cheng Zhang', ' Yu Zhu', ' Qingsen Yan', 'Shaolin Su'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['blind-image-quality-assessment'] | ['computer-vision'] | [ 4.92204577e-01 -3.54551762e-01 2.71859527e-01 -4.56404865e-01
-8.38297784e-01 -5.69360316e-01 4.77477074e-01 -1.75381035e-01
-4.11585838e-01 5.27278006e-01 1.27990216e-01 -8.62273350e-02
-1.24928243e-01 -7.28044331e-01 -7.92778373e-01 -5.97725689e-01
1.39979467e-01 2.87304729e-01 1.99820474e-01 -3.10957134... | [11.883524894714355, -1.8292521238327026] |
9a37ca47-f7b3-419c-8eda-934c604e2b83 | pstnet-point-spatio-temporal-convolution-on-1 | 2205.13713 | null | https://arxiv.org/abs/2205.13713v1 | https://arxiv.org/pdf/2205.13713v1.pdf | PSTNet: Point Spatio-Temporal Convolution on Point Cloud Sequences | Point cloud sequences are irregular and unordered in the spatial dimension while exhibiting regularities and order in the temporal dimension. Therefore, existing grid based convolutions for conventional video processing cannot be directly applied to spatio-temporal modeling of raw point cloud sequences. In this paper, ... | ['Mohan Kankanhalli', 'Yi Yang', 'Yuhang Ding', 'Xin Yu', 'Hehe Fan'] | 2022-05-27 | pstnet-point-spatio-temporal-convolution-on | https://openreview.net/forum?id=O3bqkf_Puys | https://openreview.net/pdf?id=O3bqkf_Puys | iclr-2021-1 | ['3d-human-action-recognition'] | ['computer-vision'] | [-2.76381914e-02 -7.25200415e-01 1.24250270e-01 -3.25461984e-01
1.79346874e-02 -5.25105476e-01 6.21620476e-01 -8.78689215e-02
-2.79062510e-01 1.87210962e-01 -3.74273807e-02 -3.63068193e-01
-1.30542338e-01 -9.55485165e-01 -8.03543746e-01 -6.14826500e-01
-3.21777046e-01 3.45234811e-01 4.65661079e-01 3.64642031... | [8.406068801879883, -2.067034959793091] |
1137c404-a32a-475d-a450-faa89e65fe1f | unsupervised-multi-object-segmentation-using | 2205.13271 | null | https://arxiv.org/abs/2205.13271v2 | https://arxiv.org/pdf/2205.13271v2.pdf | Unsupervised Multi-object Segmentation Using Attention and Soft-argmax | We introduce a new architecture for unsupervised object-centric representation learning and multi-object detection and segmentation, which uses a translation-equivariant attention mechanism to predict the coordinates of the objects present in the scene and to associate a feature vector to each object. A transformer enc... | ['Arnaud de La Fortelle', 'Bruno Sauvalle'] | 2022-05-26 | null | null | null | null | ['unsupervised-object-segmentation'] | ['computer-vision'] | [ 1.60983875e-01 -1.68552458e-01 3.44247818e-02 -2.06324339e-01
-7.34101236e-01 -2.66231209e-01 5.50822198e-01 -8.67644697e-03
-3.85452896e-01 2.31027052e-01 -8.79563466e-02 9.95914936e-02
3.34181011e-01 -5.88054001e-01 -1.01130140e+00 -5.75770080e-01
6.85769841e-02 9.50523555e-01 7.60787249e-01 1.45339265... | [9.127053260803223, 0.2579821050167084] |
cbe51eb3-3394-4d60-931c-51e08e8ef4d7 | adjustable-visual-appearance-for | 2306.01344 | null | https://arxiv.org/abs/2306.01344v1 | https://arxiv.org/pdf/2306.01344v1.pdf | Adjustable Visual Appearance for Generalizable Novel View Synthesis | We present a generalizable novel view synthesis method where it is possible to modify the visual appearance of rendered views to match a target weather or lighting condition. Our method is based on a generalizable transformer architecture, trained on synthetically generated scenes under different appearance conditions.... | ['Fredrik Kahl', 'Marcel Büsching', 'Che-Tsung Lin', 'David Nilsson', 'Josef Bengtson'] | 2023-06-02 | null | null | null | null | ['novel-view-synthesis'] | ['computer-vision'] | [ 2.75756538e-01 -6.97020814e-02 3.44049901e-01 -3.67678225e-01
-3.39503050e-01 -1.05016375e+00 7.61241257e-01 -5.16776800e-01
2.81471044e-01 4.61492568e-01 2.40915686e-01 -2.67196655e-01
4.45010573e-01 -6.76151335e-01 -8.91274989e-01 -3.18412632e-01
1.86536610e-01 2.10633650e-01 3.13001335e-01 -1.39979035... | [9.30609130859375, -2.98089337348938] |
5271c2f7-6bc1-47d8-8262-20595dbe6ec6 | high-dimensional-statistical-estimation-under | 2202.13157 | null | https://arxiv.org/abs/2202.13157v4 | https://arxiv.org/pdf/2202.13157v4.pdf | High Dimensional Statistical Estimation under Uniformly Dithered One-bit Quantization | In this paper, we propose a uniformly dithered 1-bit quantization scheme for high-dimensional statistical estimation. The scheme contains truncation, dithering, and quantization as typical steps. As canonical examples, the quantization scheme is applied to the estimation problems of sparse covariance matrix estimation,... | ['Di Wang', 'Michael K. Ng', 'Cheng-Long Wang', 'Junren Chen'] | 2022-02-26 | null | null | null | null | ['low-rank-matrix-completion'] | ['methodology'] | [ 7.65553892e-01 1.45669252e-01 -4.40900475e-01 -2.67763555e-01
-1.27386570e+00 -3.47600251e-01 2.64747292e-01 1.35132149e-01
-3.58073652e-01 8.43815863e-01 3.62747729e-01 -2.27757409e-01
-2.83479571e-01 -5.25730312e-01 -1.00053716e+00 -9.95590627e-01
-3.70098531e-01 3.85145187e-01 -1.59698457e-01 1.86214298... | [6.931488037109375, 4.529102802276611] |
71d29a30-7eaf-4261-a64d-2348ad7ba9ec | majority-rule-better-patching-via-self | 2306.00108 | null | https://arxiv.org/abs/2306.00108v1 | https://arxiv.org/pdf/2306.00108v1.pdf | Majority Rule: better patching via Self-Consistency | Large Language models (LLMs) can be induced to solve non-trivial problems with "few-shot" prompts including illustrative problem-solution examples. Now if the few-shots also include "chain of thought" (CoT) explanations, which are of the form problem-explanation-solution, LLMs will generate a "explained" solution, and ... | ['Premkumar Devanbu', 'Toufique Ahmed'] | 2023-05-31 | null | null | null | null | ['program-repair', 'program-repair'] | ['computer-code', 'reasoning'] | [ 0.23314472 0.6641451 -0.39493126 -0.23927633 -1.092112 -0.27668485
0.40382552 0.4068613 0.46037173 0.51211786 0.39492947 -0.7038273
-0.31369945 -0.42073566 -1.0097634 -0.2724784 -0.03994587 0.47643995
0.12624148 -0.42110822 0.8146838 -0.27457875 -1.84366 0.72156197
0.9883091 -0.04655772 0.4... | [7.716716766357422, 7.733617782592773] |
b939e5e4-6d43-4681-bf28-d0986a327f30 | does-my-multimodal-model-learn-cross-modal | 2010.06572 | null | https://arxiv.org/abs/2010.06572v1 | https://arxiv.org/pdf/2010.06572v1.pdf | Does my multimodal model learn cross-modal interactions? It's harder to tell than you might think! | Modeling expressive cross-modal interactions seems crucial in multimodal tasks, such as visual question answering. However, sometimes high-performing black-box algorithms turn out to be mostly exploiting unimodal signals in the data. We propose a new diagnostic tool, empirical multimodally-additive function projection ... | ['Lillian Lee', 'Jack Hessel'] | 2020-10-13 | null | https://aclanthology.org/2020.emnlp-main.62 | https://aclanthology.org/2020.emnlp-main.62.pdf | emnlp-2020-11 | ['image-text-classification'] | ['miscellaneous'] | [ 4.38914955e-01 6.81644753e-02 -2.13587403e-01 -2.49517500e-01
-9.48235750e-01 -8.08475673e-01 1.14088571e+00 7.36930296e-02
-3.62342775e-01 5.52600861e-01 4.94881213e-01 -5.35244465e-01
-1.15694746e-01 -1.93997234e-01 -8.79580855e-01 -9.29529011e-01
1.08216092e-01 5.16991317e-01 -2.69426346e-01 -6.86207786... | [10.879786491394043, 1.612903356552124] |
a3c50dc5-ca53-46e1-9189-8543b4154112 | document-image-classification-with-intra | 1801.09321 | null | http://arxiv.org/abs/1801.09321v3 | http://arxiv.org/pdf/1801.09321v3.pdf | Document Image Classification with Intra-Domain Transfer Learning and Stacked Generalization of Deep Convolutional Neural Networks | In this work, a region-based Deep Convolutional Neural Network framework is
proposed for document structure learning. The contribution of this work
involves efficient training of region based classifiers and effective
ensembling for document image classification. A primary level of `inter-domain'
transfer learning is u... | ['Swapan Kumar Parui', 'Ujjwal Bhattacharya', 'Saikat Roy', 'Arindam Das'] | 2018-01-29 | null | null | null | null | ['document-image-classification'] | ['computer-vision'] | [ 2.27669492e-01 -7.92133734e-02 -4.48824525e-01 -6.44507766e-01
-8.79865468e-01 -7.84715474e-01 8.28323543e-01 3.69833380e-01
-2.20340818e-01 4.27233636e-01 -7.29575977e-02 -5.32797217e-01
-9.17573646e-02 -8.59524965e-01 -9.97177482e-01 -5.38294733e-01
-1.29373699e-01 4.76266265e-01 3.65519553e-01 -2.47063667... | [11.454648971557617, 2.6130287647247314] |
d577b3c0-0c7d-4c13-bfcc-de7b7030effe | reliable-lexical-simplification-for-non | null | null | https://aclanthology.org/N15-2002 | https://aclanthology.org/N15-2002.pdf | Reliable Lexical Simplification for Non-Native Speakers | null | ['Gustavo Paetzold'] | 2015-06-01 | null | null | null | naacl-2015-6 | ['complex-word-identification'] | ['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... | [-7.328296184539795, 3.64961838722229] |
01aaed08-10cf-41ab-9854-d902f9f86957 | unsupervised-deep-video-denoising | 2011.15045 | null | https://arxiv.org/abs/2011.15045v3 | https://arxiv.org/pdf/2011.15045v3.pdf | Unsupervised Deep Video Denoising | Deep convolutional neural networks (CNNs) for video denoising are typically trained with supervision, assuming the availability of clean videos. However, in many applications, such as microscopy, noiseless videos are not available. To address this, we propose an Unsupervised Deep Video Denoiser (UDVD), a CNN architectu... | ['Carlos Fernandez-Granda', 'Eero P. Simoncelli', 'Mitesh M. Khapra', 'Peter A. Crozier', 'Ramon Manzorro', 'Joshua L. Vincent', 'Sreyas Mohan', 'Dev Yashpal Sheth'] | 2020-11-30 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Sheth_Unsupervised_Deep_Video_Denoising_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Sheth_Unsupervised_Deep_Video_Denoising_ICCV_2021_paper.pdf | iccv-2021-1 | ['video-denoising'] | ['computer-vision'] | [ 4.70833540e-01 -2.31947213e-01 3.12855273e-01 -1.97513118e-01
-6.30726993e-01 -5.28191447e-01 3.45250517e-01 -1.88992694e-01
-8.98956001e-01 7.05048800e-01 1.03576027e-01 5.53099625e-03
2.19813958e-01 -3.75065923e-01 -1.04551446e+00 -1.05586004e+00
9.81757343e-02 -3.24471947e-03 1.00648813e-01 -1.01240762... | [11.50440788269043, -2.1845409870147705] |
950d7429-c1bd-47c9-94ab-cd5655a0b284 | nbmod-find-it-and-grasp-it-in-noisy | 2306.10265 | null | https://arxiv.org/abs/2306.10265v1 | https://arxiv.org/pdf/2306.10265v1.pdf | NBMOD: Find It and Grasp It in Noisy Background | Grasping objects is a fundamental yet important capability of robots, and many tasks such as sorting and picking rely on this skill. The prerequisite for stable grasping is the ability to correctly identify suitable grasping positions. However, finding appropriate grasping points is challenging due to the diverse shape... | ['Qianqiu Tan', 'Yuchen Liu', 'Baohua Zhang', 'Congmin Guo', 'Xinyu Zhou', 'Boyuan Cao'] | 2023-06-17 | null | null | null | null | ['robotic-grasping'] | ['robots'] | [-2.07643852e-01 -5.39914668e-01 1.29591629e-01 -1.63790047e-01
-1.69242948e-01 -7.17008770e-01 -6.59395941e-03 1.03192814e-01
-1.06182352e-01 2.23065302e-01 -5.11781812e-01 -2.74283960e-02
-3.17426383e-01 -8.76543701e-01 -8.41579616e-01 -1.02906930e+00
-3.61134022e-01 5.37533641e-01 6.24531090e-01 -3.38758945... | [5.845089912414551, -0.9252120852470398] |
75516527-0957-43a8-9a0a-eef483ca6f35 | arrhythmia-detection-using-deep-convolutional | null | null | https://www.researchgate.net/publication/327602644_Arrhythmia_Detection_Using_Deep_Convolutional_Neural_Network_With_Long_Duration_ECG_Signals | https://www.researchgate.net/publication/327602644_Arrhythmia_Detection_Using_Deep_Convolutional_Neural_Network_With_Long_Duration_ECG_Signals | Arrhythmia Detection Using Deep Convolutional Neural Network With Long Duration ECG Signals | This article presents a new deep learning approach for cardiac arrhythmia (17 classes) detection based on long-duration electrocardiography (ECG) signal analysis. Cardiovascular disease prevention is one of the most important tasks of any health care system as about 50 million people are at risk of heart disease in the... | ['Ru-San Tan', 'Rajendra Acharya', 'Pawel Plawiak', 'Ozal Yildirima'] | 2018-09-01 | null | null | null | computers-in-biology-and-medicine-2018-9 | ['arrhythmia-detection', 'electrocardiography-ecg'] | ['medical', 'methodology'] | [ 7.58105516e-02 -4.10313934e-01 2.73909956e-01 -1.92762077e-01
-5.83169878e-01 -3.91410768e-01 -1.88722163e-01 3.78586382e-01
-5.61142564e-01 7.64186203e-01 -3.53475064e-01 -5.01202047e-01
-2.37663820e-01 -6.60649300e-01 -7.61945471e-02 -4.83939379e-01
-3.78619492e-01 3.09772193e-01 -3.28765154e-01 -8.03460646... | [14.224437713623047, 3.28597354888916] |
3ae4a2a2-7a58-4e1a-91ff-1fc04c2a8b28 | 190407094 | 1904.07094 | null | https://arxiv.org/abs/1904.07094v3 | https://arxiv.org/pdf/1904.07094v3.pdf | CEDR: Contextualized Embeddings for Document Ranking | Although considerable attention has been given to neural ranking architectures recently, far less attention has been paid to the term representations that are used as input to these models. In this work, we investigate how two pretrained contextualized language models (ELMo and BERT) can be utilized for ad-hoc document... | ['Nazli Goharian', 'Andrew Yates', 'Sean MacAvaney', 'Arman Cohan'] | 2019-04-15 | null | null | null | null | ['ad-hoc-information-retrieval'] | ['natural-language-processing'] | [ 2.14282721e-01 -2.39871383e-01 -3.38365346e-01 -6.86611712e-01
-1.28052044e+00 -5.69833457e-01 9.67624545e-01 4.38757986e-01
-9.90255594e-01 4.35004324e-01 6.98557675e-01 -4.30016786e-01
-3.94604474e-01 -5.25361538e-01 -5.94812214e-01 -5.40003292e-02
-3.23300332e-01 6.43620729e-01 2.08789080e-01 -4.70178545... | [11.433893203735352, 7.659843444824219] |
8ed142f3-84c5-483d-8777-ac871ff501db | a-real-time-speaker-diarization-system-based | 2107.09321 | null | https://arxiv.org/abs/2107.09321v1 | https://arxiv.org/pdf/2107.09321v1.pdf | A Real-time Speaker Diarization System Based on Spatial Spectrum | In this paper we describe a speaker diarization system that enables localization and identification of all speakers present in a conversation or meeting. We propose a novel systematic approach to tackle several long-standing challenges in speaker diarization tasks: (1) to segment and separate overlapping speech from tw... | ['Zhijie Yan', 'Jinwei Feng', 'Hongbin Suo', 'Xianliang Wang', 'Weilong Huang', 'Siqi Zheng'] | 2021-07-20 | null | null | null | null | ['speaker-identification'] | ['speech'] | [ 2.31069610e-01 -1.77297279e-01 1.77737832e-01 -3.41778636e-01
-1.48942757e+00 -6.62987113e-01 3.51837665e-01 1.98105186e-01
-5.91782890e-02 2.42473468e-01 3.61105770e-01 -2.40405485e-01
7.53147751e-02 -1.18172325e-01 -1.39952406e-01 -9.52315450e-01
-3.61729652e-01 5.77867270e-01 2.12352768e-01 6.66110739... | [14.812801361083984, 5.87849235534668] |
f7d425cf-8517-4c35-a126-1a8392b54f7a | influence-guided-data-augmentation-for-neural | 2108.10248 | null | https://arxiv.org/abs/2108.10248v1 | https://arxiv.org/pdf/2108.10248v1.pdf | Influence-guided Data Augmentation for Neural Tensor Completion | How can we predict missing values in multi-dimensional data (or tensors) more accurately? The task of tensor completion is crucial in many applications such as personalized recommendation, image and video restoration, and link prediction in social networks. Many tensor factorization and neural network-based tensor comp... | ['Srijan Kumar', 'Ryan A. Rossi', 'Sungchul Kim', 'Sejoon Oh'] | 2021-08-23 | null | null | null | null | ['video-restoration'] | ['computer-vision'] | [-1.37972012e-01 -1.89730361e-01 -4.02845412e-01 -2.99180835e-01
-2.88372815e-01 -3.46762151e-01 1.73168018e-01 -1.38451010e-01
-7.72444680e-02 7.75863290e-01 9.03223634e-01 -2.05918267e-01
-4.96691346e-01 -6.93829417e-01 -8.48904431e-01 -5.68159223e-01
-2.23998845e-01 5.50117671e-01 -3.96118492e-01 -1.98823124... | [7.167637348175049, 4.6819658279418945] |
59520f5d-a4af-4a00-a507-fa88a1fce0af | tcam-temporal-class-activation-maps-for | 2208.14542 | null | https://arxiv.org/abs/2208.14542v2 | https://arxiv.org/pdf/2208.14542v2.pdf | TCAM: Temporal Class Activation Maps for Object Localization in Weakly-Labeled Unconstrained Videos | Weakly supervised video object localization (WSVOL) allows locating object in videos using only global video tags such as object class. State-of-art methods rely on multiple independent stages, where initial spatio-temporal proposals are generated using visual and motion cues, then prominent objects are identified and ... | ['Eric Granger', 'Luke McCaffrey', 'Ismail Ben Ayed', 'Soufiane Belharbi'] | 2022-08-30 | null | null | null | null | ['visual-object-tracking'] | ['computer-vision'] | [-9.55423992e-03 -3.58954757e-01 -5.75994134e-01 -2.87042469e-01
-8.91659200e-01 -6.07063413e-01 4.43773627e-01 -9.20272619e-02
-6.25463665e-01 5.74057937e-01 -1.43914744e-01 1.69383839e-01
1.78623885e-01 -4.40725267e-01 -1.00767446e+00 -8.34723473e-01
-3.11022699e-01 -4.07813899e-02 7.08365262e-01 3.96190375... | [9.112367630004883, -0.020607739686965942] |
01621f73-aafe-47e2-a10d-4d4d902b92e7 | generalized-classification-of-satellite-image | 2203.09175 | null | https://arxiv.org/abs/2203.09175v2 | https://arxiv.org/pdf/2203.09175v2.pdf | Generalized Classification of Satellite Image Time Series with Thermal Positional Encoding | Large-scale crop type classification is a task at the core of remote sensing efforts with applications of both economic and ecological importance. Current state-of-the-art deep learning methods are based on self-attention and use satellite image time series (SITS) to discriminate crop types based on their unique growth... | ['Ira Assent', 'Charlotte Pelletier', 'Joachim Nyborg'] | 2022-03-17 | null | null | null | null | ['crop-classification'] | ['miscellaneous'] | [ 3.11149985e-01 -6.69511616e-01 -1.79219931e-01 -4.17498440e-01
-1.78157523e-01 -8.45648527e-01 3.83623779e-01 4.47812140e-01
-2.71372557e-01 6.58465266e-01 1.01968022e-02 -6.12288117e-01
-3.11351389e-01 -1.27500188e+00 -1.03844833e+00 -8.08309495e-01
-3.95232916e-01 -1.56786174e-01 -1.20302670e-01 -6.00228250... | [9.432027816772461, -1.5780121088027954] |
643a3aab-67f1-4724-8a83-eab7f044de5e | task-formulation-for-extracting-social | 2301.11386 | null | https://arxiv.org/abs/2301.11386v1 | https://arxiv.org/pdf/2301.11386v1.pdf | Task formulation for Extracting Social Determinants of Health from Clinical Narratives | Objective: The 2022 n2c2 NLP Challenge posed identification of social determinants of health (SDOH) in clinical narratives. We present three systems that we developed for the Challenge and discuss the distinctive task formulation used in each of the three systems. Materials and Methods: The first system identifies targ... | ['Daniel S. Zisook', 'Elly W. Yang', 'Paul Wang', 'Son Doan', 'Ian M. Finn', 'Manabu Torii'] | 2023-01-26 | null | null | null | null | ['event-extraction'] | ['natural-language-processing'] | [ 4.62974489e-01 8.38908195e-01 -4.70533490e-01 5.86324325e-03
-1.22683811e+00 -6.40954614e-01 5.40587127e-01 7.72535622e-01
-5.65374136e-01 9.18658614e-01 4.61777568e-01 -5.74742317e-01
-6.85147047e-01 -3.24066252e-01 -3.32183063e-01 -5.47941267e-01
5.10520360e-04 6.15781486e-01 1.64455608e-01 -1.57639667... | [8.52079963684082, 8.835460662841797] |
e2a93470-578c-4b44-8ad0-23756b7d1bc9 | causal-effect-estimation-recent-advances | 2302.00848 | null | https://arxiv.org/abs/2302.00848v1 | https://arxiv.org/pdf/2302.00848v1.pdf | Causal Effect Estimation: Recent Advances, Challenges, and Opportunities | Causal inference has numerous real-world applications in many domains, such as health care, marketing, political science, and online advertising. Treatment effect estimation, a fundamental problem in causal inference, has been extensively studied in statistics for decades. However, traditional treatment effect estimati... | ['Sheng Li', 'Wei Chu', 'Ruopeng Li', 'Jianmin Huang', 'Zhixuan Chu'] | 2023-02-02 | null | null | null | null | ['marketing'] | ['miscellaneous'] | [ 5.81298053e-01 2.61325333e-02 -9.77904260e-01 -3.96155179e-01
-7.53439844e-01 -1.06686726e-01 5.80339372e-01 5.54714382e-01
-1.30173773e-01 1.04498637e+00 7.57272661e-01 -4.15716738e-01
-5.07038057e-01 -9.70633209e-01 -5.26044965e-01 -7.97770917e-01
-1.19990245e-01 3.23471665e-01 -2.01922745e-01 6.52118400... | [8.0391206741333, 5.387927055358887] |
1c94a224-ff62-4d90-8d7d-4759d4326c71 | data-driven-optimal-power-flow-a-physics | 2006.00544 | null | https://arxiv.org/abs/2006.00544v1 | https://arxiv.org/pdf/2006.00544v1.pdf | Data-driven Optimal Power Flow: A Physics-Informed Machine Learning Approach | This paper proposes a data-driven approach for optimal power flow (OPF) based on the stacked extreme learning machine (SELM) framework. SELM has a fast training speed and does not require the time-consuming parameter tuning process compared with the deep learning algorithms. However, the direct application of SELM for ... | ['Junbo Zhao', 'Juan Yu', 'Hongxin Yu', 'Xingyu Lei', 'Qian Gao', 'Zhifang Yang'] | 2020-05-31 | null | null | null | null | ['physics-informed-machine-learning'] | ['graphs'] | [-1.15042947e-01 -1.41817763e-01 -4.73047495e-01 -3.50899369e-01
-3.58475685e-01 -1.71775818e-01 2.68681735e-01 6.83373809e-02
2.21776776e-03 7.39804983e-01 -4.98554945e-01 -4.35499430e-01
-8.26379478e-01 -5.57829320e-01 -2.87719041e-01 -9.76117313e-01
-2.18636811e-01 3.06149989e-01 -2.12404922e-01 -1.72294632... | [5.881826877593994, 2.906022310256958] |
f421bbb0-fc98-4a9f-bf3d-d8e6578445d4 | project-then-transfer-effective-two-stage | null | null | https://aclanthology.org/2021.eacl-main.221 | https://aclanthology.org/2021.eacl-main.221.pdf | Project-then-Transfer: Effective Two-stage Cross-lingual Transfer for Semantic Dependency Parsing | This paper describes the first report on cross-lingual transfer for semantic dependency parsing. We present the insight that there are twodifferent kinds of cross-linguality, namely sur-face level and mantic level, and try to cap-ture both kinds of cross-linguality by combin-ing annotation projection and model transfer... | ['Toshinori Miyoshi', 'Terufumi Morishita', 'Gaku Morio', 'Hiroaki Ozaki'] | 2021-04-01 | null | null | null | eacl-2021-2 | ['semantic-dependency-parsing'] | ['natural-language-processing'] | [-6.00790143e-01 5.90845108e-01 -5.62823415e-01 -6.22278690e-01
-1.25149286e+00 -7.45863497e-01 5.23009896e-01 7.23682344e-03
-3.49482000e-01 8.11461270e-01 3.20584804e-01 -5.92689693e-01
4.84764606e-01 -5.55946887e-01 -7.73605525e-01 -1.57234803e-01
-1.02381252e-01 4.96638298e-01 4.23611879e-01 -3.87187988... | [10.475801467895508, 9.76611042022705] |
7df5667c-031e-466a-98a7-c6f0b623b83a | assessment-of-algorithms-for-mitosis | 1411.5825 | null | http://arxiv.org/abs/1411.5825v1 | http://arxiv.org/pdf/1411.5825v1.pdf | Assessment of algorithms for mitosis detection in breast cancer histopathology images | The proliferative activity of breast tumors, which is routinely estimated by
counting of mitotic figures in hematoxylin and eosin stained histology
sections, is considered to be one of the most important prognostic markers.
However, mitosis counting is laborious, subjective and may suffer from low
inter-observer agreem... | ['Josien P. W. Pluim', 'Max A. Viergever', 'Ching-Wei Wang', 'Thomas Walter', 'Jürgen Schmidhuber', 'Dan C. Cireşan', 'Anders B. L. Larsen', 'Angel Cruz-Roa', 'Stefan M. Willems', 'Teofilo E. de Campos', 'Paul J. van Diest', 'Mitko Veta', 'Miangela M. Lacle', 'Anant Madabhushi', 'Satoshi Kondo', 'Nasir M. Rajpoot', 'Jo... | 2014-11-21 | null | null | null | null | ['mitosis-detection'] | ['medical'] | [ 5.08399010e-02 1.25438988e-01 -4.36597943e-01 -6.33492395e-02
-1.16840458e+00 -6.16213143e-01 3.79718363e-01 9.78211701e-01
-7.01776326e-01 8.84213984e-01 1.92269180e-02 -2.83891886e-01
2.71535188e-01 -4.03168976e-01 -5.82018755e-02 -1.15911937e+00
9.77306161e-04 9.96835172e-01 4.67455328e-01 3.54553938... | [15.0613374710083, -3.146801710128784] |
7f47bfb1-01c0-4c40-b9b8-959fcdf031aa | cmc-v2-towards-more-accurate-covid-19 | 2211.14557 | null | https://arxiv.org/abs/2211.14557v1 | https://arxiv.org/pdf/2211.14557v1.pdf | CMC v2: Towards More Accurate COVID-19 Detection with Discriminative Video Priors | This paper presents our solution for the 2nd COVID-19 Competition, occurring in the framework of the AIMIA Workshop at the European Conference on Computer Vision (ECCV 2022). In our approach, we employ the winning solution last year which uses a strong 3D Contrastive Mixup Classifcation network (CMC v1) as the baseline... | ['Rui Feng', 'Xiaobo Zhang', 'Yuejie Zhang', 'Yi Wang', 'Nan Zhang', 'Jilan Xu', 'Junlin Hou'] | 2022-11-26 | null | null | null | null | ['covid-19-detection'] | ['medical'] | [ 3.49530578e-01 1.45694882e-01 -1.25616238e-01 7.34652132e-02
-7.89565384e-01 -5.07191539e-01 8.46849620e-01 -3.80236618e-02
-6.01933420e-01 4.00637180e-01 1.29478812e-01 1.09461211e-01
2.14865282e-01 -2.73349583e-01 -8.00859332e-01 -4.13164735e-01
2.16426551e-01 3.02199066e-01 4.77825284e-01 -1.35736421... | [8.872897148132324, -0.11668851226568222] |
497e28e0-4a6c-48ab-be93-15178d98fed4 | can-we-find-neurons-that-cause-unrealistic | 2201.06346 | null | https://arxiv.org/abs/2201.06346v4 | https://arxiv.org/pdf/2201.06346v4.pdf | Can We Find Neurons that Cause Unrealistic Images in Deep Generative Networks? | Even though Generative Adversarial Networks (GANs) have shown a remarkable ability to generate high-quality images, GANs do not always guarantee the generation of photorealistic images. Occasionally, they generate images that have defective or unnatural objects, which are referred to as 'artifacts'. Research to investi... | ['Jaesik Choi', 'Wonjoon Chang', 'Hwanil Choi'] | 2022-01-17 | null | null | null | null | ['gan-image-forensics'] | ['computer-vision'] | [ 4.29815680e-01 2.67362535e-01 4.15161252e-01 -1.02227367e-01
-3.25689644e-01 -6.91799164e-01 6.74875557e-01 -4.34127420e-01
7.39713311e-02 9.68792200e-01 6.03311732e-02 -6.02603145e-02
2.19189048e-01 -8.39481831e-01 -8.81059766e-01 -9.00924087e-01
4.56766307e-01 -7.68977106e-02 -1.02075502e-01 -8.79790355... | [11.656780242919922, -0.44420403242111206] |
b0fc09b5-4e81-4523-8328-fd97a9e1f3d3 | rtmdet-an-empirical-study-of-designing-real | 2212.07784 | null | https://arxiv.org/abs/2212.07784v2 | https://arxiv.org/pdf/2212.07784v2.pdf | RTMDet: An Empirical Study of Designing Real-Time Object Detectors | In this paper, we aim to design an efficient real-time object detector that exceeds the YOLO series and is easily extensible for many object recognition tasks such as instance segmentation and rotated object detection. To obtain a more efficient model architecture, we explore an architecture that has compatible capacit... | ['Kai Chen', 'Shilong Zhang', 'Yanyi Liu', 'Yudong Wang', 'Yue Zhou', 'Haian Huang', 'Wenwei Zhang', 'Chengqi Lyu'] | 2022-12-14 | null | null | null | null | ['real-time-instance-segmentation', 'object-detection-in-aerial-images', 'real-time-object-detection'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-1.43851802e-01 -4.94951427e-01 -2.80326217e-01 -3.71500641e-01
-5.88053226e-01 -4.47922111e-01 4.25320715e-02 -1.77702636e-01
-5.42379439e-01 -5.16989492e-02 -8.96874011e-01 -4.57531303e-01
4.15347874e-01 -5.29570222e-01 -8.24926198e-01 -6.56063080e-01
-6.73900619e-02 3.68852496e-01 9.27189112e-01 8.67502689... | [8.733859062194824, -0.25068241357803345] |
381b52fd-2219-4280-a3c4-59344e139846 | learning-discriminative-illumination-and | null | null | http://openaccess.thecvf.com/content_cvpr_2013/html/Liu_Learning_Discriminative_Illumination_2013_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2013/papers/Liu_Learning_Discriminative_Illumination_2013_CVPR_paper.pdf | Learning Discriminative Illumination and Filters for Raw Material Classification with Optimal Projections of Bidirectional Texture Functions | We present a computational imaging method for raw material classification using features of Bidirectional Texture Functions (BTF). Texture is an intrinsic feature for many materials, such as wood, fabric, and granite. At appropriate scales, even "uniform" materials will also exhibit texture features that can be helpful... | ['Chao Liu', 'Geifei Yang', 'Jinwei Gu'] | 2013-06-01 | null | null | null | cvpr-2013-6 | ['material-classification'] | ['computer-vision'] | [ 4.38167512e-01 -5.64895391e-01 3.30311805e-01 -3.83589298e-01
-2.79784590e-01 -5.47363400e-01 4.54962850e-01 -2.74840385e-01
1.58742025e-01 7.56110549e-01 1.25811279e-01 2.97585521e-02
-6.17597163e-01 -9.83470261e-01 -4.41506773e-01 -1.10082114e+00
1.21908143e-01 2.70880163e-01 2.35348150e-01 -8.37769657... | [10.003211975097656, -2.651890277862549] |
625fe872-ea5e-4fac-9f10-e246f9591d2c | bass-boosting-abstractive-summarization-with | 2105.12041 | null | https://arxiv.org/abs/2105.12041v1 | https://arxiv.org/pdf/2105.12041v1.pdf | BASS: Boosting Abstractive Summarization with Unified Semantic Graph | Abstractive summarization for long-document or multi-document remains challenging for the Seq2Seq architecture, as Seq2Seq is not good at analyzing long-distance relations in text. In this paper, we present BASS, a novel framework for Boosting Abstractive Summarization based on a unified Semantic graph, which aggregate... | ['Haifeng Wang', 'Hua Wu', 'Sujian Li', 'Ziqiang Cao', 'Jiachen Liu', 'Xinyan Xiao', 'Wei Li', 'Wenhao Wu'] | 2021-05-25 | null | https://aclanthology.org/2021.acl-long.472 | https://aclanthology.org/2021.acl-long.472.pdf | acl-2021-5 | ['implicit-relations'] | ['natural-language-processing'] | [ 4.49201137e-01 5.81027925e-01 -3.95590007e-01 -4.07691270e-01
-1.07012296e+00 -5.67389250e-01 5.78566968e-01 7.38075078e-01
-7.89294243e-02 9.18666124e-01 1.39740694e+00 -1.08039491e-01
-9.53562465e-03 -7.42307842e-01 -6.51782870e-01 -2.44573355e-01
-1.33642927e-01 3.30239356e-01 7.45822862e-02 -4.97834593... | [12.5098876953125, 9.49025821685791] |
3e8e0133-8efc-425d-90ea-70f90f0ee904 | fair-k-center-clustering-for-data | 1901.08628 | null | https://arxiv.org/abs/1901.08628v2 | https://arxiv.org/pdf/1901.08628v2.pdf | Fair k-Center Clustering for Data Summarization | In data summarization we want to choose $k$ prototypes in order to summarize a data set. We study a setting where the data set comprises several demographic groups and we are restricted to choose $k_i$ prototypes belonging to group $i$. A common approach to the problem without the fairness constraint is to optimize a c... | ['Matthäus Kleindessner', 'Pranjal Awasthi', 'Jamie Morgenstern'] | 2019-01-24 | null | null | null | null | ['data-summarization'] | ['miscellaneous'] | [-2.75507152e-01 3.03003371e-01 -3.79535496e-01 -5.66074550e-01
-7.69128442e-01 -5.66194892e-01 -7.34333396e-02 8.75966668e-01
-7.40110219e-01 4.74577099e-01 5.28826416e-02 -1.61470339e-01
-3.94468993e-01 -9.97499228e-01 -6.49483502e-01 -4.74600077e-01
-2.96383947e-01 8.86291862e-01 -4.21530642e-02 2.89794635... | [6.642625331878662, 4.935529708862305] |
b309e036-05b2-4e37-9b83-2d1db2513964 | universal-representation-for-code | 2103.03116 | null | https://arxiv.org/abs/2103.03116v1 | https://arxiv.org/pdf/2103.03116v1.pdf | Universal Representation for Code | Learning from source code usually requires a large amount of labeled data. Despite the possible scarcity of labeled data, the trained model is highly task-specific and lacks transferability to different tasks. In this work, we present effective pre-training strategies on top of a novel graph-based code representation, ... | ['Srinivasan Sengamedu', 'George Karypis', 'Hoan Nguyen', 'Linfeng Liu'] | 2021-03-04 | null | null | null | null | ['method-name-prediction'] | ['natural-language-processing'] | [ 1.40148833e-01 2.30432466e-01 -7.30854571e-01 -4.17139739e-01
-6.91759825e-01 -7.68031001e-01 4.19004440e-01 5.27755797e-01
3.83688927e-01 2.05778360e-01 1.77606747e-01 -7.43997514e-01
1.03410922e-01 -7.97036111e-01 -8.52550209e-01 -1.09052233e-01
-4.18370306e-01 3.82134807e-03 2.43735060e-01 -3.72484922... | [7.531672954559326, 7.881263256072998] |
f1cc7a78-9c41-4ae2-b915-0c22cb63fecf | review-machine-learning-techniques-for | 1712.04391 | null | http://arxiv.org/abs/1712.04391v2 | http://arxiv.org/pdf/1712.04391v2.pdf | Review. Machine learning techniques for traffic sign detection | An automatic road sign detection system localizes road signs from within
images captured by an on-board camera of a vehicle, and support the driver to
properly ride the vehicle. Most existing algorithms include a preprocessing
step, feature extraction and detection step. This paper arranges the methods
applied to road ... | ['Ying Wang', 'Rinat Mukhometzianov'] | 2017-12-12 | null | null | null | null | ['traffic-sign-detection'] | ['computer-vision'] | [ 3.37798804e-01 -3.65844399e-01 -8.44062865e-01 -5.91494143e-01
-1.33870557e-01 -2.90467680e-01 7.08233178e-01 -9.09840882e-01
-5.80581129e-01 4.65795666e-01 -2.43613705e-01 -8.06442320e-01
-1.40375167e-01 -5.90390801e-01 -1.71589673e-01 -8.72821569e-01
2.30599970e-01 -1.18549764e-01 4.95071411e-01 -3.04463357... | [7.96988582611084, -0.8346313834190369] |
f4cc5947-51df-4da1-8231-38c4934bd5c4 | understanding-expertise-through | 2302.07457 | null | https://arxiv.org/abs/2302.07457v2 | https://arxiv.org/pdf/2302.07457v2.pdf | Understanding Expertise through Demonstrations: A Maximum Likelihood Framework for Offline Inverse Reinforcement Learning | Offline inverse reinforcement learning (Offline IRL) aims to recover the structure of rewards and environment dynamics that underlie observed actions in a fixed, finite set of demonstrations from an expert agent. Accurate models of expertise in executing a task has applications in safety-sensitive applications such as ... | ['Mingyi Hong', 'Alfredo Garcia', 'Chenliang Li', 'Siliang Zeng'] | 2023-02-15 | null | null | null | null | ['continuous-control', 'd4rl'] | ['playing-games', 'robots'] | [ 1.98439024e-02 3.93114418e-01 -5.97045362e-01 -8.39761347e-02
-8.73358071e-01 -4.67126817e-01 2.96163082e-01 1.14014946e-01
-8.41827035e-01 1.13359785e+00 1.32156322e-02 -1.41552567e-01
-5.50627410e-01 -3.08823884e-01 -1.15906823e+00 -7.71706343e-01
-3.12169641e-01 5.94681740e-01 -1.37334585e-01 -3.71797718... | [4.159605026245117, 2.2401249408721924] |
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