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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
add37c0e-e70b-4150-be09-02b48593970b | towards-end-to-end-unified-scene-text | 2203.15143 | null | https://arxiv.org/abs/2203.15143v2 | https://arxiv.org/pdf/2203.15143v2.pdf | Towards End-to-End Unified Scene Text Detection and Layout Analysis | Scene text detection and document layout analysis have long been treated as two separate tasks in different image domains. In this paper, we bring them together and introduce the task of unified scene text detection and layout analysis. The first hierarchical scene text dataset is introduced to enable this novel resear... | ['Michalis Raptis', 'Yasuhisa Fujii', 'Alessandro Bissacco', 'Dmitry Panteleev', 'Siyang Qin', 'Shangbang Long'] | 2022-03-28 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Long_Towards_End-to-End_Unified_Scene_Text_Detection_and_Layout_Analysis_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Long_Towards_End-to-End_Unified_Scene_Text_Detection_and_Layout_Analysis_CVPR_2022_paper.pdf | cvpr-2022-1 | ['document-layout-analysis', 'scene-text-detection'] | ['computer-vision', 'computer-vision'] | [-1.92942508e-02 -7.83182800e-01 4.73388396e-02 -2.60200500e-01
-7.62429595e-01 -6.27700388e-01 7.80926049e-01 2.00668827e-01
-1.55240893e-01 -2.38142535e-01 3.66712928e-01 -2.91099906e-01
3.31661731e-01 -4.54963326e-01 -4.80400741e-01 -5.42298615e-01
5.44896185e-01 3.76435697e-01 5.26561141e-01 2.06488177... | [12.02387523651123, 2.2239794731140137] |
59918c1c-5274-4260-91e9-6e465c55477e | modeling-intelligent-decision-making-command | 1903.08412 | null | http://arxiv.org/abs/1903.08412v1 | http://arxiv.org/pdf/1903.08412v1.pdf | Modeling Intelligent Decision Making Command And Control Agents: An Application to Air Defense | The paper is a half-way between the agent technology and the mathematical
reasoning to model tactical decision making tasks. These models are applied to
air defense (AD) domain for command and control (C2). It also addresses the
issues related to evaluation of agents. The agents are designed and implemented
using the a... | ['Sumanta Kumar Das'] | 2019-03-20 | null | null | null | null | ['mathematical-reasoning'] | ['natural-language-processing'] | [-1.78500637e-01 5.52187562e-01 -1.64559275e-01 -1.03560507e-01
4.35657531e-01 -1.09101915e+00 1.38540936e+00 3.02833200e-01
-7.75996327e-01 8.84944618e-01 -4.15797438e-03 -1.15265894e+00
-6.69234335e-01 -9.07975078e-01 2.47622296e-01 -3.88521016e-01
-5.90928495e-01 1.04573715e+00 1.57716230e-01 -1.12468290... | [3.835899829864502, 1.7008708715438843] |
3bdd2031-70c9-44f5-b054-a2a8e3b32472 | mitigating-biased-activation-in-weakly | 2305.15354 | null | https://arxiv.org/abs/2305.15354v1 | https://arxiv.org/pdf/2305.15354v1.pdf | Mitigating Biased Activation in Weakly-supervised Object Localization via Counterfactual Learning | In this paper, we focus on an under-explored issue of biased activation in prior weakly-supervised object localization methods based on Class Activation Mapping (CAM). We analyze the cause of this problem from a causal view and attribute it to the co-occurring background confounders. Following this insight, we propose ... | ['Jun Xiao', 'Yi Yang', 'Ping Liu', 'Lei Chen', 'Yawei Luo', 'Feifei Shao'] | 2023-05-24 | null | null | null | null | ['object-localization', 'weakly-supervised-object-localization'] | ['computer-vision', 'computer-vision'] | [ 7.19301581e-01 4.17014629e-01 -3.84642988e-01 -1.71190515e-01
-4.04750764e-01 -3.08335155e-01 9.55971599e-01 -1.18922509e-01
-3.45028102e-01 8.61969411e-01 3.42697531e-01 -4.98641163e-01
6.16600104e-02 -8.06986868e-01 -1.05256772e+00 -8.46710205e-01
-1.24840662e-01 4.77756461e-04 1.92354739e-01 1.24434695... | [9.886211395263672, 1.7273269891738892] |
cd0e5cca-76e4-4633-818d-0473044fc2fc | deep-depth-completion-a-survey | 2205.05335 | null | https://arxiv.org/abs/2205.05335v3 | https://arxiv.org/pdf/2205.05335v3.pdf | Deep Depth Completion from Extremely Sparse Data: A Survey | Depth completion aims at predicting dense pixel-wise depth from an extremely sparse map captured from a depth sensor, e.g., LiDARs. It plays an essential role in various applications such as autonomous driving, 3D reconstruction, augmented reality, and robot navigation. Recent successes on the task have been demonstrat... | ['Tin Lun Lam', 'Honghai Liu', 'Qing Gao', 'Chenyou Fan', 'Mete Ozay', 'Chenyu Bao', 'Junjie Hu'] | 2022-05-11 | null | null | null | null | ['depth-completion'] | ['computer-vision'] | [ 3.77747148e-01 1.52696073e-02 -4.87749785e-01 -7.52597511e-01
-6.59781754e-01 -1.23425767e-01 4.14430857e-01 -1.99560031e-01
-5.62776685e-01 7.67592430e-01 1.25245541e-01 -2.59206980e-01
-1.75752252e-01 -7.95882940e-01 -6.86955690e-01 -5.48208594e-01
-3.37544501e-01 2.09339797e-01 -1.93447247e-02 -5.06955795... | [8.539555549621582, -2.6261703968048096] |
9086e637-892d-47b2-a74f-9359d2da2c1e | 3d-pose-transfer-with-correspondence-learning | 2109.15025 | null | https://arxiv.org/abs/2109.15025v6 | https://arxiv.org/pdf/2109.15025v6.pdf | 3D Pose Transfer with Correspondence Learning and Mesh Refinement | 3D pose transfer is one of the most challenging 3D generation tasks. It aims to transfer the pose of a source mesh to a target mesh and keep the identity (e.g., body shape) of the target mesh. Some previous works require key point annotations to build reliable correspondence between the source and target meshes, while ... | ['Guosheng Lin', 'Fayao Liu', 'Ruibo Li', 'Jiacheng Wei', 'Chaoyue Song'] | 2021-09-30 | null | http://proceedings.neurips.cc/paper/2021/hash/18a411989b47ed75a60ac69d9da05aa5-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/18a411989b47ed75a60ac69d9da05aa5-Paper.pdf | neurips-2021-12 | ['pose-transfer'] | ['computer-vision'] | [ 2.11110964e-01 -1.14412475e-02 1.37566119e-01 -3.14120054e-01
-4.52848256e-01 -2.55847305e-01 3.79593819e-01 -1.25966191e-01
-1.78426117e-01 5.65233231e-01 1.31260464e-02 4.21575993e-01
2.27848187e-01 -1.00087023e+00 -9.37615633e-01 -6.12390280e-01
2.55464524e-01 6.74883246e-01 5.60251594e-01 -3.49818558... | [7.264889240264893, -1.4791414737701416] |
f66f9b61-8330-44d9-9379-9653094bec25 | impact-analysis-of-the-use-of-speech-and | null | null | https://aclanthology.org/2022.lrec-1.316 | https://aclanthology.org/2022.lrec-1.316.pdf | Impact Analysis of the Use of Speech and Language Models Pretrained by Self-Supersivion for Spoken Language Understanding | Pretrained models through self-supervised learning have been recently introduced for both acoustic and language modeling. Applied to spoken language understanding tasks, these models have shown their great potential by improving the state-of-the-art performances on challenging benchmark datasets. In this paper, we pres... | ['Yannick Estève', 'Nathalie Camelin', 'Bassam Jabaian', 'Sahar Ghannay', 'Gaëlle Laperriere', 'Antoine Caubrière', 'Valentin Pelloin', 'Salima Mdhaffar'] | null | null | null | null | lrec-2022-6 | ['spoken-language-understanding', 'slot-filling', 'spoken-language-understanding'] | ['natural-language-processing', 'natural-language-processing', 'speech'] | [ 7.85982087e-02 4.47838634e-01 2.31209770e-02 -6.37837887e-01
-9.94623721e-01 -1.81534782e-01 6.90017700e-01 4.34398204e-01
-9.86833990e-01 6.89067781e-01 2.11438090e-01 -2.33561292e-01
1.61611751e-01 -4.95710850e-01 -8.75452936e-01 -4.13023740e-01
1.22284450e-01 9.49778676e-01 4.43153948e-01 -4.51174825... | [13.839347839355469, 6.807491779327393] |
d4ddcfbb-e734-41d9-a56d-c040ff47b26b | explainable-end-to-end-deep-learning-for | null | null | https://doi.org/10.1117/1.JMI.7.4.044503 | https://www.spiedigitallibrary.org/journalArticle/Download?fullDOI=10.1117%2F1.JMI.7.4.044503 | Explainable end-to-end deep learning for diabetic retinopathy detection across multiple datasets | Purpose: Diabetic retinopathy (DR) is characterized by retinal lesions affecting people having diabetes for several years. It is one of the leading causes of visual impairment worldwide. To diagnose this disease, ophthalmologists need to manually analyze retinal fundus images. Computer-aided diagnosis systems can help ... | ['Moulay A. Akhloufi', 'Mohamed Chetoui'] | 2020-08-20 | null | null | null | null | ['diabetic-retinopathy-detection', 'diabetic-retinopathy-grading'] | ['medical', 'medical'] | [-2.33641088e-01 8.76266044e-03 1.25467598e-01 -4.49925423e-01
-2.16390193e-01 -2.73045093e-01 1.60244673e-01 -1.15384020e-01
-3.90604019e-01 8.07208300e-01 -5.94980456e-02 -4.07664299e-01
-3.80189717e-01 -7.15199769e-01 -2.55287588e-01 -6.93144143e-01
-3.05932853e-02 2.29002714e-01 -4.25201990e-02 1.40717342... | [15.857301712036133, -4.003580570220947] |
29082770-ac92-43c7-aa93-b7300fdc8fab | hand-drawn-symbol-recognition-of-surgical | 2006.16546 | null | https://arxiv.org/abs/2006.16546v1 | https://arxiv.org/pdf/2006.16546v1.pdf | Hand-drawn Symbol Recognition of Surgical Flowsheet Graphs with Deep Image Segmentation | Perioperative data are essential to investigating the causes of adverse surgical outcomes. In some low to middle income countries, these data are computationally inaccessible due to a lack of digitization of surgical flowsheets. In this paper, we present a deep image segmentation approach using a U-Net architecture tha... | ['Marcel Durieux', 'Donald Brown', 'William Adorno III', 'Angela Yi'] | 2020-06-30 | null | null | null | null | ['template-matching'] | ['computer-vision'] | [ 3.69772822e-01 4.91053730e-01 -4.46465045e-01 -1.57416865e-01
-6.32058084e-01 -5.31160176e-01 7.01780170e-02 7.17645168e-01
-6.22499526e-01 5.98206520e-01 4.05595042e-02 -9.24056351e-01
-1.13441281e-01 -8.80468190e-01 -5.82216322e-01 -1.56280935e-01
-1.16151057e-01 3.82395685e-01 -6.66726902e-02 1.10370718... | [14.406479835510254, -2.63227915763855] |
ac269534-eba3-4fb2-ada5-6621e93175cb | a-multi-stream-deep-neural-network-with-late | null | null | https://doi.org/10.1016/j.eswa.2022.117030 | https://reader.elsevier.com/reader/sd/pii/S0957417422004468?token=0DF82822FFD4CF570219D85B4D22640194174214B62C2EF2F9D312C3B9A94571E27EDA274DE53CE91F489F49976617E2&originRegion=eu-west-1&originCreation=20220420222220 | A multi-stream deep neural network with late fuzzy fusion for real-world anomaly detection | Abnormal event detection in video is alternatively known as outlier detection, where machine learning can be highly effective. While testing an unknown video, the objective of such methods is to verify the video’s category, e.g. normal or abnormal. This paper exploits visual information from normal as well as abnormal ... | ['Ig-JaeKim', 'Heeseung Choi', 'Debi Prosad Dogra', 'Nitin Sharma', 'Kamalakar Vijay Thakare'] | 2022-03-27 | null | null | null | expert-systems-with-applications-2022-3 | ['anomaly-detection-in-surveillance-videos', 'abnormal-event-detection-in-video', 'anomaly-detection-in-surveillance-videos', 'abnormal-event-detection-in-video'] | ['computer-vision', 'computer-vision', 'methodology', 'methodology'] | [ 2.88349062e-01 -2.58192599e-01 4.45111170e-02 -1.96179345e-01
-5.38678110e-01 -3.20229977e-01 4.79845107e-01 4.37543362e-01
-3.38081032e-01 4.21867341e-01 -1.08537741e-01 -1.39933363e-01
-2.45641038e-01 -6.08848214e-01 -7.26397574e-01 -7.41242111e-01
-4.87525403e-01 -6.95142224e-02 3.14275861e-01 7.14364871... | [7.826423168182373, 1.5839239358901978] |
5e1c19c7-181e-465b-b010-664daaab7bab | bangla-grammatical-error-detection-using-t5 | 2303.10612 | null | https://arxiv.org/abs/2303.10612v1 | https://arxiv.org/pdf/2303.10612v1.pdf | Bangla Grammatical Error Detection Using T5 Transformer Model | This paper presents a method for detecting grammatical errors in Bangla using a Text-to-Text Transfer Transformer (T5) Language Model, using the small variant of BanglaT5, fine-tuned on a corpus of 9385 sentences where errors were bracketed by the dedicated demarcation symbol. The T5 model was primarily designed for tr... | ['Khondker Salman Sayeed', 'H. A. Z. Sameen Shahgir'] | 2023-03-19 | null | null | null | null | ['grammatical-error-detection'] | ['natural-language-processing'] | [ 9.44248587e-02 1.25391394e-01 6.24459505e-01 -6.65061176e-01
-1.23765981e+00 -4.00112391e-01 1.65356070e-01 3.70705336e-01
-4.85066384e-01 6.61281049e-01 -1.01281172e-02 -7.65332997e-01
6.37752339e-02 -7.49157965e-01 -6.98288262e-01 -1.07853726e-01
8.98584053e-02 7.44404912e-01 3.37625474e-01 -6.76832795... | [11.066154479980469, 10.62102222442627] |
7e9304a9-034a-41fc-b91b-182eb32e9f79 | facial-synthesizing-dynamic-talking-face-with | 2108.07938 | null | https://arxiv.org/abs/2108.07938v1 | https://arxiv.org/pdf/2108.07938v1.pdf | FACIAL: Synthesizing Dynamic Talking Face with Implicit Attribute Learning | In this paper, we propose a talking face generation method that takes an audio signal as input and a short target video clip as reference, and synthesizes a photo-realistic video of the target face with natural lip motions, head poses, and eye blinks that are in-sync with the input audio signal. We note that the synthe... | ['Xiaohu Guo', 'Madhukar Budagavi', 'Saifeng Ni', 'Ming Zeng', 'Yifei HUANG', 'Yifan Zhao', 'Chenxu Zhang'] | 2021-08-18 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Zhang_FACIAL_Synthesizing_Dynamic_Talking_Face_With_Implicit_Attribute_Learning_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Zhang_FACIAL_Synthesizing_Dynamic_Talking_Face_With_Implicit_Attribute_Learning_ICCV_2021_paper.pdf | iccv-2021-1 | ['3d-face-animation', 'talking-face-generation'] | ['computer-vision', 'computer-vision'] | [ 9.89992693e-02 9.72638354e-02 1.41999438e-01 -4.40438569e-01
-6.92953348e-01 -4.58678842e-01 4.72927153e-01 -9.63806510e-01
3.16115618e-01 6.31457984e-01 4.07675177e-01 3.72362822e-01
4.91408288e-01 -4.50933784e-01 -9.59569752e-01 -8.45610976e-01
2.62518615e-01 1.61738053e-01 -1.27637088e-01 -4.18857932... | [13.200947761535645, -0.4139115810394287] |
466a2086-556a-4468-8091-fa0b65c3ab21 | faster-ltn-a-neuro-symbolic-end-to-end-object | 2107.01877 | null | https://arxiv.org/abs/2107.01877v1 | https://arxiv.org/pdf/2107.01877v1.pdf | Faster-LTN: a neuro-symbolic, end-to-end object detection architecture | The detection of semantic relationships between objects represented in an image is one of the fundamental challenges in image interpretation. Neural-Symbolic techniques, such as Logic Tensor Networks (LTNs), allow the combination of semantic knowledge representation and reasoning with the ability to efficiently learn f... | ['Fabrizio Lamberti', 'Lia Morra', 'Filomeno Davide Miro', 'Francesco Manigrasso'] | 2021-07-05 | null | null | null | null | ['tensor-networks'] | ['methodology'] | [ 3.45683992e-01 5.93748987e-01 -8.08064565e-02 -6.41296983e-01
-2.01980919e-01 -2.66214550e-01 7.91648567e-01 1.37019873e-01
-3.50581497e-01 3.73705328e-02 3.93343233e-02 -4.69198227e-01
-3.59790146e-01 -7.32003212e-01 -9.98080790e-01 -5.40953279e-02
-2.36287743e-01 6.06104791e-01 1.75137654e-01 -2.78189898... | [10.5514497756958, 2.211491107940674] |
4791d68c-eee6-4009-9bc3-c96315bad49d | learning-sequence-descriptor-based-on | 2305.11467 | null | https://arxiv.org/abs/2305.11467v1 | https://arxiv.org/pdf/2305.11467v1.pdf | Learning Sequence Descriptor based on Spatiotemporal Attention for Visual Place Recognition | Sequence-based visual place recognition (sVPR) aims to match frame sequences with frames stored in a reference map for localization. Existing methods include sequence matching and sequence descriptor-based retrieval. The former is based on the assumption of constant velocity, which is difficult to hold in real scenario... | ['Chen Ye', 'Wenjie Mu', 'Gengxuan Tian', 'Yingfeng Cai', 'Junqiao Zhao', 'Fenglin Zhang'] | 2023-05-19 | null | null | null | null | ['visual-place-recognition'] | ['computer-vision'] | [ 4.19749096e-02 -8.80234778e-01 -4.35907006e-01 -2.41625518e-01
-7.10869491e-01 -5.93830168e-01 7.79209614e-01 1.58317491e-01
-5.55248797e-01 5.80194414e-01 2.97441691e-01 6.61073267e-01
-2.21316323e-01 -5.61965704e-01 -5.14855385e-01 -1.00037956e+00
-1.36585161e-01 -2.35285893e-01 7.33495891e-01 -1.71818405... | [7.950123310089111, -1.3689895868301392] |
0ea58a89-6c47-4a88-bf48-5dbfaf738b50 | learning-montezumas-revenge-from-a-single | 1812.03381 | null | http://arxiv.org/abs/1812.03381v1 | http://arxiv.org/pdf/1812.03381v1.pdf | Learning Montezuma's Revenge from a Single Demonstration | We propose a new method for learning from a single demonstration to solve
hard exploration tasks like the Atari game Montezuma's Revenge. Instead of
imitating human demonstrations, as proposed in other recent works, our approach
is to maximize rewards directly. Our agent is trained using off-the-shelf
reinforcement lea... | ['Tim Salimans', 'Richard Chen'] | 2018-12-08 | null | null | null | null | ['montezumas-revenge'] | ['playing-games'] | [ 1.04929313e-01 5.56922376e-01 4.13611624e-03 8.16667378e-02
-9.54964399e-01 -7.97185779e-01 6.13163650e-01 -6.31718934e-02
-9.90767479e-01 1.27524543e+00 -1.50957704e-01 -4.12631601e-01
-3.56040942e-03 -5.85701764e-01 -9.95908737e-01 -6.87571824e-01
-4.48076844e-01 7.39872932e-01 1.43001035e-01 -3.60990733... | [4.0217061042785645, 1.6628053188323975] |
b410e9f1-4417-49ce-8fbe-9519352dfe93 | complex-gated-recurrent-neural-networks | 1806.08267 | null | http://arxiv.org/abs/1806.08267v2 | http://arxiv.org/pdf/1806.08267v2.pdf | Complex Gated Recurrent Neural Networks | Complex numbers have long been favoured for digital signal processing, yet
complex representations rarely appear in deep learning architectures. RNNs,
widely used to process time series and sequence information, could greatly
benefit from complex representations. We present a novel complex gated
recurrent cell, which i... | ['Moritz Wolter', 'Angela Yao'] | 2018-06-21 | complex-gated-recurrent-neural-networks-1 | http://papers.nips.cc/paper/8253-complex-gated-recurrent-neural-networks | http://papers.nips.cc/paper/8253-complex-gated-recurrent-neural-networks.pdf | neurips-2018-12 | ['music-transcription'] | ['music'] | [ 3.84102404e-01 -4.36370701e-01 -3.87579352e-02 -5.25391735e-02
-4.02967155e-01 -2.15644553e-01 8.45726252e-01 -2.67667979e-01
-6.79204822e-01 9.14803028e-01 3.49704087e-01 -3.09888124e-01
3.49665105e-01 -6.50511563e-01 -3.92973304e-01 -9.13363695e-01
-4.04986978e-01 2.08999470e-01 2.46428058e-01 -3.71799588... | [7.488323211669922, 3.413301467895508] |
465f991e-d9df-4611-9fa6-a0301eb48971 | multi-accdoa-localizing-and-detecting | 2110.07124 | null | https://arxiv.org/abs/2110.07124v2 | https://arxiv.org/pdf/2110.07124v2.pdf | Multi-ACCDOA: Localizing and Detecting Overlapping Sounds from the Same Class with Auxiliary Duplicating Permutation Invariant Training | Sound event localization and detection (SELD) involves identifying the direction-of-arrival (DOA) and the event class. The SELD methods with a class-wise output format make the model predict activities of all sound event classes and corresponding locations. The class-wise methods can output activity-coupled Cartesian D... | ['Yuki Mitsufuji', 'Emiru Tsunoo', 'Naoya Takahashi', 'Shusuke Takahashi', 'Yuichiro Koyama', 'Kazuki Shimada'] | 2021-10-14 | null | null | null | null | ['sound-event-localization-and-detection'] | ['audio'] | [ 1.26305178e-01 -3.19964290e-01 4.37569380e-01 6.89824671e-03
-1.17544472e+00 -6.16145015e-01 4.13255006e-01 3.46765220e-02
-3.83481801e-01 6.58379316e-01 2.49510370e-02 -1.86665341e-01
-6.30591750e-01 -5.75938106e-01 -9.05921936e-01 -9.11025047e-01
-3.43672872e-01 3.11609864e-01 7.34380305e-01 3.85026187... | [15.192118644714355, 5.249176025390625] |
857610b6-eb7b-41c1-ad17-4206c602824c | correlative-information-maximization-a | 2306.04810 | null | https://arxiv.org/abs/2306.04810v2 | https://arxiv.org/pdf/2306.04810v2.pdf | Correlative Information Maximization: A Biologically Plausible Approach to Supervised Deep Neural Networks without Weight Symmetry | The backpropagation algorithm has experienced remarkable success in training large-scale artificial neural networks, however, its biological-plausibility is disputed, and it remains an open question whether the brain employs supervised learning mechanisms akin to it. Here, we propose correlative information maximizatio... | ['Alper T Erdogan', 'Cengiz Pehlevan', 'Bariscan Bozkurt'] | 2023-06-07 | null | null | null | null | ['open-question'] | ['natural-language-processing'] | [ 5.23698211e-01 4.77035671e-01 8.73850733e-02 -5.53084314e-01
1.50315925e-01 -1.09948039e-01 8.17193210e-01 2.44466104e-02
-8.58368158e-01 8.66519034e-01 -4.83580772e-03 -6.44238412e-01
-5.90027094e-01 -5.85196078e-01 -5.99966288e-01 -1.04152870e+00
-1.04827240e-01 2.71185040e-01 1.63864657e-01 -2.71228075... | [8.082579612731934, 3.3808465003967285] |
8e9a0dd0-31b6-4e7c-bd88-f831cd809970 | a-self-paced-bci-system-with-low-latency-for | 2204.05450 | null | https://arxiv.org/abs/2204.05450v1 | https://arxiv.org/pdf/2204.05450v1.pdf | A self-paced BCI system with low latency for motor imagery onset detection based on time series prediction paradigm | In a self-paced motor-imagery brain-computer interface (MI-BCI), the onsets of the MI commands presented in a continuous electroencephalogram (EEG) signal are unknown. To detect these onsets, most self-paced approaches apply a window function on the continuous EEG signal and split it into long segments for further anal... | ['Elnaz Banan Sadeghian', 'Navid Ayoobi'] | 2022-04-12 | null | null | null | null | ['time-series-prediction'] | ['time-series'] | [ 6.11206770e-01 -3.00772756e-01 -7.25568756e-02 -2.75179267e-01
-5.59964657e-01 -3.36504616e-02 4.01412815e-01 1.13904593e-03
-7.13099062e-01 8.05571914e-01 1.36488199e-01 -1.09861881e-01
-1.70340911e-01 -3.06514531e-01 -7.17059672e-01 -4.22261298e-01
-5.77629149e-01 2.28898004e-02 1.89872637e-01 6.64524958... | [13.087825775146484, 3.459249973297119] |
0432ee6b-cea8-4727-a652-8a14e96643d9 | revisiting-personalized-federated-learning | 2302.01677 | null | https://arxiv.org/abs/2302.01677v2 | https://arxiv.org/pdf/2302.01677v2.pdf | Revisiting Personalized Federated Learning: Robustness Against Backdoor Attacks | In this work, besides improving prediction accuracy, we study whether personalization could bring robustness benefits to backdoor attacks. We conduct the first study of backdoor attacks in the pFL framework, testing 4 widely used backdoor attacks against 6 pFL methods on benchmark datasets FEMNIST and CIFAR-10, a total... | ['Minhao Cheng', 'Bolin Ding', 'Yaliang Li', 'Daoyuan Chen', 'Liuyi Yao', 'Zeyu Qin'] | 2023-02-03 | null | null | null | null | ['personalized-federated-learning'] | ['methodology'] | [-3.57256174e-01 -5.60624003e-01 -7.26750314e-01 -7.62975737e-02
-7.35351443e-01 -1.37736154e+00 3.55428547e-01 -9.16260108e-02
-1.84856400e-01 6.82470262e-01 7.01133683e-02 -1.02306366e+00
-1.30006969e-01 -8.82048845e-01 -8.58606160e-01 -4.35744077e-01
-3.79885703e-01 -1.34424075e-01 2.21905947e-01 -3.79954457... | [5.787728786468506, 7.384841442108154] |
91c5460a-9e57-4095-8aa8-86c86bca11e9 | panic-3d-stylized-single-view-3d | 2303.14587 | null | https://arxiv.org/abs/2303.14587v1 | https://arxiv.org/pdf/2303.14587v1.pdf | PAniC-3D: Stylized Single-view 3D Reconstruction from Portraits of Anime Characters | We propose PAniC-3D, a system to reconstruct stylized 3D character heads directly from illustrated (p)ortraits of (ani)me (c)haracters. Our anime-style domain poses unique challenges to single-view reconstruction; compared to natural images of human heads, character portrait illustrations have hair and accessories with... | ['Matthias Zwicker', 'Xiao Yang', 'Janus Kristjansson', 'Sizhe An', 'Guoxian Song', 'Yiheng Zhu', 'Heng Wang', 'Yichun Shi', 'Kevin Zhang', 'Shuhong Chen'] | 2023-03-25 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Chen_PAniC-3D_Stylized_Single-View_3D_Reconstruction_From_Portraits_of_Anime_Characters_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Chen_PAniC-3D_Stylized_Single-View_3D_Reconstruction_From_Portraits_of_Anime_Characters_CVPR_2023_paper.pdf | cvpr-2023-1 | ['single-view-3d-reconstruction'] | ['computer-vision'] | [ 1.18940890e-01 4.65263017e-02 1.54307798e-01 -1.90270960e-01
-4.83466119e-01 -1.07893109e+00 7.39077687e-01 -6.93805218e-01
5.26096165e-01 5.09151280e-01 1.57895237e-02 -3.30193341e-01
5.40401757e-01 -6.74331903e-01 -9.11357462e-01 -3.24679255e-01
3.27189237e-01 9.78296578e-01 -5.42455018e-02 -3.39955360... | [12.323234558105469, -0.5785751342773438] |
61250486-7771-4aca-a25d-f05bad3410fc | two-in-one-a-model-hijacking-attack-against | 2305.07406 | null | https://arxiv.org/abs/2305.07406v1 | https://arxiv.org/pdf/2305.07406v1.pdf | Two-in-One: A Model Hijacking Attack Against Text Generation Models | Machine learning has progressed significantly in various applications ranging from face recognition to text generation. However, its success has been accompanied by different attacks. Recently a new attack has been proposed which raises both accountability and parasitic computing risks, namely the model hijacking attac... | ['Ahmed Salem', 'Yang Zhang', 'Michael Backes', 'Wai Man Si'] | 2023-05-12 | null | null | null | null | ['face-recognition', 'text-summarization'] | ['computer-vision', 'natural-language-processing'] | [ 6.02406085e-01 -8.67871046e-02 4.87999544e-02 -4.64854799e-02
-6.43503010e-01 -9.32067394e-01 1.07704413e+00 -9.94124785e-02
-2.67131776e-01 5.92296958e-01 -2.49593914e-01 -7.53788531e-01
3.66317213e-01 -7.82016456e-01 -5.30106425e-01 -7.01649010e-01
2.89140910e-01 1.70270130e-01 1.76817775e-01 -6.57151104... | [5.918694972991943, 7.799533367156982] |
884dc1a9-45f8-41c7-966d-0b3cf79998f5 | margin-based-parallel-corpus-mining-with | 1811.01136 | null | https://arxiv.org/abs/1811.01136v2 | https://arxiv.org/pdf/1811.01136v2.pdf | Margin-based Parallel Corpus Mining with Multilingual Sentence Embeddings | Machine translation is highly sensitive to the size and quality of the training data, which has led to an increasing interest in collecting and filtering large parallel corpora. In this paper, we propose a new method for this task based on multilingual sentence embeddings. In contrast to previous approaches, which rely... | ['Mikel Artetxe', 'Holger Schwenk'] | 2018-11-03 | margin-based-parallel-corpus-mining-with-1 | https://aclanthology.org/P19-1309 | https://aclanthology.org/P19-1309.pdf | acl-2019-7 | ['parallel-corpus-mining', 'cross-lingual-bitext-mining'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.17007607e-01 -6.33126870e-02 -3.41978043e-01 -3.54729265e-01
-1.41326439e+00 -7.90196776e-01 1.12626970e+00 8.77957404e-01
-1.05318475e+00 1.06233776e+00 4.79942411e-01 -5.20850718e-01
-1.08588412e-01 -5.10764182e-01 -8.70167613e-01 -6.46517098e-01
2.72234589e-01 8.14037979e-01 3.13916445e-01 -4.42765921... | [11.373188972473145, 10.23267650604248] |
f0a477bd-1493-4767-a30f-565d7f99a201 | conformal-language-modeling | 2306.10193 | null | https://arxiv.org/abs/2306.10193v1 | https://arxiv.org/pdf/2306.10193v1.pdf | Conformal Language Modeling | We propose a novel approach to conformal prediction for generative language models (LMs). Standard conformal prediction produces prediction sets -- in place of single predictions -- that have rigorous, statistical performance guarantees. LM responses are typically sampled from the model's predicted distribution over th... | ['Regina Barzilay', 'Tommi S. Jaakkola', 'Jae Ho Sohn', 'Adam Yala', 'Tal Schuster', 'Adam Fisch', 'Victor Quach'] | 2023-06-16 | null | null | null | null | ['conformal-prediction', 'question-answering', 'text-summarization', 'open-domain-question-answering', 'conformal-prediction'] | ['computer-vision', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'reasoning'] | [ 6.36611760e-01 9.55592096e-01 -1.34102866e-01 -4.18722600e-01
-1.84886742e+00 -7.50988305e-01 3.43470007e-01 3.66436541e-01
-4.46303003e-02 9.20487642e-01 6.87124074e-01 -1.81750521e-01
-1.24667943e-01 -8.06405842e-01 -1.00914693e+00 -6.08467340e-01
1.73201159e-01 1.08459294e+00 1.01935789e-01 1.31816730... | [11.992600440979004, 9.056231498718262] |
01ab4758-c134-41e5-8526-7f509c049df5 | connecting-vision-and-language-with-video | 2302.11217 | null | https://arxiv.org/abs/2302.11217v2 | https://arxiv.org/pdf/2302.11217v2.pdf | Connecting Vision and Language with Video Localized Narratives | We propose Video Localized Narratives, a new form of multimodal video annotations connecting vision and language. In the original Localized Narratives, annotators speak and move their mouse simultaneously on an image, thus grounding each word with a mouse trace segment. However, this is challenging on a video. Our new ... | ['Vittorio Ferrari', 'Radu Soricut', 'Jordi Pont-Tuset', 'Soravit Changpinyo', 'Paul Voigtlaender'] | 2023-02-22 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Voigtlaender_Connecting_Vision_and_Language_With_Video_Localized_Narratives_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Voigtlaender_Connecting_Vision_and_Language_With_Video_Localized_Narratives_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-narrative-grounding', 'video-question-answering'] | ['computer-vision', 'computer-vision'] | [ 5.26593253e-02 -8.94089118e-02 -4.75973904e-01 -7.55986497e-02
-8.49494874e-01 -1.06414127e+00 7.08117008e-01 6.27069548e-02
-5.28206348e-01 5.17782211e-01 6.30488336e-01 6.19015433e-02
3.61569405e-01 -9.26714912e-02 -8.91576111e-01 -3.59605312e-01
-2.02622637e-01 1.99012533e-02 3.57070416e-01 1.82476759... | [10.328194618225098, 0.8022266030311584] |
c9fdb885-fafc-4561-ad23-2e8721df274b | missing-entries-matrix-approximation-and | 1302.6768 | null | http://arxiv.org/abs/1302.6768v2 | http://arxiv.org/pdf/1302.6768v2.pdf | Missing Entries Matrix Approximation and Completion | We describe several algorithms for matrix completion and matrix approximation
when only some of its entries are known. The approximation constraint can be
any whose approximated solution is known for the full matrix. For low rank
approximations, similar algorithms appears recently in the literature under
different name... | ['Gil Shabat', 'Yaniv Shmueli', 'Amir Averbuch'] | 2013-02-27 | null | null | null | null | ['electrical-engineering'] | ['miscellaneous'] | [ 3.74663621e-01 1.45189971e-01 9.65396464e-02 3.69024426e-02
-5.11677742e-01 -5.46422303e-01 9.58991274e-02 1.53645441e-01
-4.56734717e-01 7.12454319e-01 2.28444040e-01 -7.80730769e-02
-5.31044364e-01 -3.88331622e-01 -7.36389995e-01 -9.17065084e-01
-1.61816344e-01 4.79640156e-01 -1.58553228e-01 -3.65856409... | [7.083764553070068, 4.443610668182373] |
9bc92634-ed36-4da7-ba4f-4ad264d3a67e | decipherment-with-a-million-random-restarts | null | null | https://aclanthology.org/D13-1087 | https://aclanthology.org/D13-1087.pdf | Decipherment with a Million Random Restarts | null | ['Taylor Berg-Kirkpatrick', 'Dan Klein'] | 2013-10-01 | null | null | null | emnlp-2013-10 | ['decipherment'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.320093154907227, 3.69858980178833] |
bffae6d8-0846-41da-8034-4e4f24147a84 | global-sensitivity-analysis-of-a-symmetric | 2107.04647 | null | https://arxiv.org/abs/2107.04647v2 | https://arxiv.org/pdf/2107.04647v2.pdf | Global sensitivity analysis of asymmetric energy harvesters | Parametric variability is inevitable in actual energy harvesters. It can significantly affect crucial aspects of the system performance, especially in harvesting systems that present geometric parameters, material properties, or excitation conditions that are susceptible to small perturbations. This work aims to develo... | ['Paulo Sérgio Varoto', 'Samuel da Silva', 'Americo Cunha Jr', 'João Pedro Norenberg'] | 2021-07-09 | null | null | null | null | ['robust-design'] | ['miscellaneous'] | [ 1.88119218e-01 -2.38799676e-01 9.13771987e-02 4.30825651e-01
2.06936244e-02 -8.95381510e-01 3.69376987e-01 1.86428994e-01
-2.49926329e-01 7.41978645e-01 3.52925472e-02 2.23561347e-01
-7.04586327e-01 -7.02419579e-01 -4.36636031e-01 -1.54348230e+00
-3.79286528e-01 5.61359003e-02 1.01583824e-01 -4.89372253... | [5.9905829429626465, 3.1897833347320557] |
896b9d67-c025-40e4-a1f1-92ed6ecfa39c | g-map-general-memory-augmented-pre-trained | 2212.03613 | null | https://arxiv.org/abs/2212.03613v2 | https://arxiv.org/pdf/2212.03613v2.pdf | G-MAP: General Memory-Augmented Pre-trained Language Model for Domain Tasks | Recently, domain-specific PLMs have been proposed to boost the task performance of specific domains (e.g., biomedical and computer science) by continuing to pre-train general PLMs with domain-specific corpora. However, this Domain-Adaptive Pre-Training (DAPT; Gururangan et al. (2020)) tends to forget the previous gener... | ['Qun Liu', 'Xin Jiang', 'Guangyong Chen', 'Lifeng Shang', 'Jiaxin Shi', 'Wei zhang', 'Yichun Yin', 'Zhongwei Wan'] | 2022-12-07 | null | null | null | null | ['general-knowledge'] | ['miscellaneous'] | [ 1.33784056e-01 1.41411990e-01 -1.40546694e-01 -2.85345733e-01
-5.21542728e-01 2.41487729e-03 5.96499145e-01 1.60062209e-01
-6.23470247e-01 1.05605304e+00 2.10755065e-01 -6.35090247e-02
4.93003279e-02 -7.59712815e-01 -7.19695926e-01 -6.80138767e-01
2.67042160e-01 6.13894880e-01 5.18656313e-01 -2.16171697... | [10.429265022277832, 8.136680603027344] |
2a3ed11a-c345-4e95-abfc-a9205bf39a90 | lamad-a-linguistic-attentional-model-for | null | null | https://aclanthology.org/2021.findings-emnlp.317 | https://aclanthology.org/2021.findings-emnlp.317.pdf | LAMAD: A Linguistic Attentional Model for Arabic Text Diacritization | In Arabic Language, diacritics are used to specify meanings as well as pronunciations. However, diacritics are often omitted from written texts, which increases the number of possible meanings and pronunciations. This leads to an ambiguous text and makes the computational process on undiacritized text more difficult. I... | ['Jianliang Gao', 'Raeed Al-Sabri'] | null | null | null | null | findings-emnlp-2021-11 | ['arabic-text-diacritization'] | ['natural-language-processing'] | [-1.37159443e-02 -4.71537620e-01 -1.64224952e-01 -3.70215476e-01
-1.32846653e-01 -5.24507701e-01 5.27946055e-01 3.96160036e-01
-5.99199891e-01 4.86818433e-01 4.72329617e-01 -3.00115049e-01
1.63274363e-01 -8.37483823e-01 -2.32978195e-01 -7.26053596e-01
4.49721247e-01 7.44344145e-02 -1.00925229e-01 -4.82710987... | [10.15091323852539, 10.131697654724121] |
db8568c1-2191-4932-b12e-b8cb772a0c86 | a-study-of-few-shot-audio-classification | 2012.01573 | null | https://arxiv.org/abs/2012.01573v1 | https://arxiv.org/pdf/2012.01573v1.pdf | A Study of Few-Shot Audio Classification | Advances in deep learning have resulted in state-of-the-art performance for many audio classification tasks but, unlike humans, these systems traditionally require large amounts of data to make accurate predictions. Not every person or organization has access to those resources, and the organizations that do, like our ... | ['Lauren Phillips', 'Brian Hutchinson', 'Chris Careaga', 'Piper Wolters'] | 2020-12-02 | null | null | null | null | ['few-shot-audio-classification'] | ['audio'] | [ 2.22151667e-01 -1.87481537e-01 -1.90330356e-01 -4.48481381e-01
-1.05936456e+00 -2.71382481e-01 1.46556929e-01 -2.84438636e-02
-4.32178468e-01 6.64550781e-01 3.50961238e-01 1.27642276e-02
7.81936273e-02 -6.04609430e-01 -3.48394394e-01 -3.59958202e-01
-1.50498658e-01 3.26643854e-01 3.99007387e-02 -1.53343737... | [14.544051170349121, 5.680128574371338] |
a5372334-9c6c-466e-bc34-aa906122bc87 | interpretable-neural-architecture-search-and | 2305.11917 | null | https://arxiv.org/abs/2305.11917v1 | https://arxiv.org/pdf/2305.11917v1.pdf | Interpretable neural architecture search and transfer learning for understanding sequence dependent enzymatic reactions | Finely-tuned enzymatic pathways control cellular processes, and their dysregulation can lead to disease. Creating predictive and interpretable models for these pathways is challenging because of the complexity of the pathways and of the cellular and genomic contexts. Here we introduce Elektrum, a deep learning framewor... | ['Olga Troyanskaya', 'Michael Shelley', 'Adam R. Lamson', 'Zijun Zhang'] | 2023-05-18 | null | null | null | null | ['architecture-search'] | ['methodology'] | [ 2.75307268e-01 -7.09337816e-02 -9.35122743e-02 -7.09196227e-03
-6.57174706e-01 -1.16895974e+00 4.38305825e-01 3.90783817e-01
-1.80414408e-01 1.10208189e+00 2.10316658e-01 -8.03904533e-01
-2.97027647e-01 -3.44909370e-01 -1.31064546e+00 -8.72202218e-01
-1.43681124e-01 5.09341776e-01 -7.58413076e-02 -1.29238158... | [4.947540760040283, 5.625450134277344] |
13f3c5eb-ecea-4acc-ad64-3c6131678176 | sart-similarity-analogies-and-relatedness-for | 1904.00365 | null | http://arxiv.org/abs/1904.00365v1 | http://arxiv.org/pdf/1904.00365v1.pdf | SART - Similarity, Analogies, and Relatedness for Tatar Language: New Benchmark Datasets for Word Embeddings Evaluation | There is a huge imbalance between languages currently spoken and
corresponding resources to study them. Most of the attention naturally goes to
the "big" languages: those which have the largest presence in terms of media
and number of speakers. Other less represented languages sometimes do not even
have a good quality ... | ['Adín Ramírez Rivera', 'Albina Khusainova', 'Adil Khan'] | 2019-03-31 | null | null | null | null | ['embeddings-evaluation'] | ['natural-language-processing'] | [-4.97660756e-01 -2.31616378e-01 -3.07485640e-01 -2.86889523e-01
-4.69098955e-01 -5.81551075e-01 8.21788907e-01 3.11877638e-01
-9.93642926e-01 5.38453460e-01 6.07510865e-01 -2.46209636e-01
-1.13388292e-01 -8.63772273e-01 -2.26557657e-01 -2.99580485e-01
2.64671803e-01 8.89974833e-01 1.17625229e-01 -8.90246868... | [10.713164329528809, 9.702906608581543] |
07066ea9-c00c-4e2b-8a7f-319b8db514b1 | transformers-for-ct-reconstruction-from | 2305.06965 | null | https://arxiv.org/abs/2305.06965v1 | https://arxiv.org/pdf/2305.06965v1.pdf | Transformers for CT Reconstruction From Monoplanar and Biplanar Radiographs | Computed Tomography (CT) scans provide detailed and accurate information of internal structures in the body. They are constructed by sending x-rays through the body from different directions and combining this information into a three-dimensional volume. Such volumes can then be used to diagnose a wide range of conditi... | ['Daniel Truhn', 'Johannes Stegmaier', 'Christiane Kuhl', 'Sven Nebelung', 'Tianyu Han', 'Gustav Müller-Franzes', 'Firas Khader'] | 2023-05-11 | null | null | null | null | ['computed-tomography-ct'] | ['methodology'] | [ 2.43666589e-01 3.45513999e-01 -8.34415182e-02 -4.11355197e-01
-7.23445237e-01 -3.60258549e-01 5.08255541e-01 1.55960992e-01
-2.88893133e-01 4.54166532e-01 3.30266118e-01 -3.08532268e-01
2.42904555e-02 -1.21647513e+00 -9.15714741e-01 -7.62234509e-01
1.39190787e-02 1.04297817e+00 -6.36225790e-02 -1.39579559... | [13.376460075378418, -2.6028034687042236] |
e54a7ecc-5e67-474f-88e7-c349e886d02d | acinoset-a-3d-pose-estimation-dataset-and | 2103.13282 | null | https://arxiv.org/abs/2103.13282v1 | https://arxiv.org/pdf/2103.13282v1.pdf | AcinoSet: A 3D Pose Estimation Dataset and Baseline Models for Cheetahs in the Wild | Animals are capable of extreme agility, yet understanding their complex dynamics, which have ecological, biomechanical and evolutionary implications, remains challenging. Being able to study this incredible agility will be critical for the development of next-generation autonomous legged robots. In particular, the chee... | ['Amir Patel', 'Mackenzie W. Mathis', 'Alexander Mathis', 'Fred Nicolls', 'Ricardo Jericevich', 'Naoya Muramatsu', 'Liam Clark', 'Daniel Joska'] | 2021-03-24 | null | null | null | null | ['animal-pose-estimation'] | ['computer-vision'] | [-1.49577469e-01 -3.68233681e-01 1.50269508e-01 -6.69580624e-02
-2.56770909e-01 -7.16072023e-01 2.18835399e-01 -1.70543045e-01
-6.76243663e-01 7.94153810e-01 -1.71247333e-01 1.28343239e-01
-5.97634055e-02 -3.75106245e-01 -8.83275092e-01 -4.86421674e-01
-8.68719637e-01 5.78928888e-01 4.72734004e-01 -4.58555996... | [7.555330753326416, -1.0780836343765259] |
3ad28049-7300-450e-a854-86df11592298 | type-supervised-sequence-labeling-based-on | 2210.10240 | null | https://arxiv.org/abs/2210.10240v2 | https://arxiv.org/pdf/2210.10240v2.pdf | Type-supervised sequence labeling based on the heterogeneous star graph for named entity recognition | Named entity recognition is a fundamental task in natural language processing, identifying the span and category of entities in unstructured texts. The traditional sequence labeling methodology ignores the nested entities, i.e. entities included in other entity mentions. Many approaches attempt to address this scenario... | ['Hong Qi', 'Yu Jiang', 'Luguang Liang', 'Haotian Tang', 'Changjiang Zhou', 'Xueru Wen'] | 2022-10-19 | null | null | null | null | ['nested-named-entity-recognition'] | ['natural-language-processing'] | [-3.54626067e-02 6.58060730e-01 -2.68626839e-01 -2.69104868e-01
-2.46096909e-01 -8.36643755e-01 4.21335906e-01 5.89899600e-01
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-1.26572296e-01 6.36045814e-01 5.53311646e-01 -2.25081459... | [9.594117164611816, 9.343138694763184] |
cfd531bf-beb6-4f6e-94ee-c9e719a6b158 | value-added-chemical-discovery-using | 1911.07630 | null | https://arxiv.org/abs/1911.07630v1 | https://arxiv.org/pdf/1911.07630v1.pdf | Value-Added Chemical Discovery Using Reinforcement Learning | Computer-assisted synthesis planning aims to help chemists find better reaction pathways faster. Finding viable and short pathways from sugar molecules to value-added chemicals can be modeled as a retrosynthesis planning problem with a catalyst allowed. This is a crucial step in efficient biomass conversion. The tradit... | ['Rajeev Surendran Assary', 'Hieu Doan', 'Sandeep Madireddy', 'Prasanna Balaprakash', 'Peihong Jiang'] | 2019-11-10 | null | null | null | null | ['retrosynthesis'] | ['medical'] | [ 3.97379994e-01 2.76411504e-01 -4.18694645e-01 1.30406231e-01
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-1.91203475e-01 6.12689078e-01 5.77942468e-02 -2.92463392... | [4.49893045425415, 6.09076452255249] |
ca1f8804-3cd0-4151-a645-bb8d5b94e5b1 | cross-lingual-transfer-for-unsupervised | null | null | https://aclanthology.org/K15-1012 | https://aclanthology.org/K15-1012.pdf | Cross-lingual Transfer for Unsupervised Dependency Parsing Without Parallel Data | null | ['Steven Bird', 'Long Duong', 'Trevor Cohn', 'Paul Cook'] | 2015-07-01 | null | null | null | conll-2015-7 | ['unsupervised-dependency-parsing'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
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-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.350193977355957, 3.6745247840881348] |
8ed4c691-6372-4693-9ad2-0db3b6711c3f | m2fpa-a-multi-yaw-multi-pitch-high-quality-1 | null | null | http://openaccess.thecvf.com/content_ICCV_2019/html/Li_M2FPA_A_Multi-Yaw_Multi-Pitch_High-Quality_Dataset_and_Benchmark_for_Facial_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Li_M2FPA_A_Multi-Yaw_Multi-Pitch_High-Quality_Dataset_and_Benchmark_for_Facial_ICCV_2019_paper.pdf | M2FPA: A Multi-Yaw Multi-Pitch High-Quality Dataset and Benchmark for Facial Pose Analysis | Facial images in surveillance or mobile scenarios often have large view-point variations in terms of pitch and yaw angles. These jointly occurred angle variations make face recognition challenging. Current public face databases mainly consider the case of yaw variations. In this paper, a new large-scale Multi-yaw Multi... | ['Pei-Pei Li', ' Zhenan Sun', ' Ran He', ' Yibo Hu', ' Xiang Wu'] | 2019-10-01 | null | null | null | iccv-2019-10 | ['robust-face-recognition'] | ['computer-vision'] | [-1.84619024e-01 -5.80075011e-02 -1.43539719e-02 -7.01396108e-01
-8.65897655e-01 -4.70419347e-01 3.69956702e-01 -1.30673349e+00
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1.92330718e-01 3.39442611e-01 -5.83916128e-01 -2.40963563... | [13.18496036529541, 0.3313773274421692] |
2f236501-e9a4-4e8d-90da-2eb4a628e29d | lite-mdetr-a-lightweight-multi-modal-detector | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Lou_Lite-MDETR_A_Lightweight_Multi-Modal_Detector_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Lou_Lite-MDETR_A_Lightweight_Multi-Modal_Detector_CVPR_2022_paper.pdf | Lite-MDETR: A Lightweight Multi-Modal Detector | Recent multi-modal detectors based on transformers and modality encoders have successfully achieved impressive results on end-to-end visual object detection conditioned on a raw text query. However, they require a large model size and an enormous amount of computations to achieve high performance, which makes it di... | ['Hongxia Jin', 'Yilin Shen', 'Ting Hua', 'Burak Uzkent', 'Yen-Chang Hsu', 'Qian Lou'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['phrase-grounding'] | ['natural-language-processing'] | [ 3.31250697e-01 -6.85625300e-02 -4.68124688e-01 -2.33567506e-01
-1.09606171e+00 -5.57043731e-01 3.23202729e-01 3.05445362e-02
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5.49088955e-01 6.04387045e-01 2.59864300e-01 -7.03890026... | [9.990898132324219, 0.9806615710258484] |
5865e954-6ccb-4784-8a06-540a526bf69b | online-conversation-disentanglement-with | 2010.11080 | null | https://arxiv.org/abs/2010.11080v1 | https://arxiv.org/pdf/2010.11080v1.pdf | Online Conversation Disentanglement with Pointer Networks | Huge amounts of textual conversations occur online every day, where multiple conversations take place concurrently. Interleaved conversations lead to difficulties in not only following the ongoing discussions but also extracting relevant information from simultaneous messages. Conversation disentanglement aims to separ... | ['Shafiq Joty', 'Tao Yu'] | 2020-10-21 | null | https://aclanthology.org/2020.emnlp-main.512 | https://aclanthology.org/2020.emnlp-main.512.pdf | emnlp-2020-11 | ['conversation-disentanglement'] | ['natural-language-processing'] | [ 2.37929717e-01 -1.21915471e-02 -2.68171638e-01 -6.52902901e-01
-1.10776532e+00 -6.27009690e-01 1.04000604e+00 1.10832088e-01
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-7.10597485e-02 -2.81079710e-01 -2.71040529e-01 -3.90506208e-01
2.59720236e-02 5.36903501e-01 -1.88295886e-01 -2.50465631... | [12.59268856048584, 7.781098365783691] |
48898cf5-89e4-4b02-a92b-69cfafaa92a4 | spectral-efficiency-analysis-of-uplink | 2212.02164 | null | https://arxiv.org/abs/2212.02164v2 | https://arxiv.org/pdf/2212.02164v2.pdf | Spectral Efficiency Analysis of Uplink-Downlink Decoupled Access in C-V2X Networks | The uplink (UL)/downlink (DL) decoupled access has been emerging as a novel access architecture to improve the performance gains in cellular networks. In this paper, we investigate the UL/DL decoupled access performance in cellular vehicle-to-everything (C-V2X). We propose a unified analytical framework for the UL/DL d... | ['Shen', 'Xuemin', 'Haibo Zhou', 'Tianqi Zhang', 'Yunting Xu', 'Kai Yu', 'Luofang Jiao'] | 2022-12-05 | null | null | null | null | ['spectral-efficiency-analysis-of-uplink'] | ['computer-code'] | [-8.87311041e-01 1.05137154e-01 -3.57021064e-01 7.55374581e-02
-4.84424442e-01 -6.78396761e-01 3.39375794e-01 -2.90075630e-01
-4.16633822e-02 1.47442591e+00 -2.74471603e-02 -1.38200104e+00
-1.92050412e-01 -6.91872180e-01 -2.38271326e-01 -1.11443841e+00
-2.63474107e-01 4.63894576e-01 -1.27735317e-01 -1.78546309... | [6.154084205627441, 1.424092173576355] |
cea09e51-fe8b-42e3-82f3-ecaf4af5bf18 | a-vision-for-semantically-enriched-data | 2303.01378 | null | https://arxiv.org/abs/2303.01378v1 | https://arxiv.org/pdf/2303.01378v1.pdf | A Vision for Semantically Enriched Data Science | The recent efforts in automation of machine learning or data science has achieved success in various tasks such as hyper-parameter optimization or model selection. However, key areas such as utilizing domain knowledge and data semantics are areas where we have seen little automation. Data Scientists have long leveraged... | ['Horst Samulowitz', 'Sainyam Galhotra', 'Kavitha Srinivas', 'Udayan Khurana'] | 2023-03-02 | null | null | null | null | ['common-sense-reasoning'] | ['reasoning'] | [-6.45216033e-02 5.89261293e-01 -6.91028714e-01 -9.17330444e-01
-3.30521852e-01 -5.81820250e-01 5.94803214e-01 9.73168492e-01
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-4.28700387e-01 -7.66264021e-01 -6.24797106e-01 6.71033710e-02
3.10181588e-01 8.15890729e-01 -9.33125168e-02 -1.27169907... | [8.97663402557373, 7.307485103607178] |
5aa6fa9e-04c1-4798-945d-673309eb1a82 | clustering-piecewise-stationary-processes | 1906.10921 | null | https://arxiv.org/abs/1906.10921v1 | https://arxiv.org/pdf/1906.10921v1.pdf | Clustering piecewise stationary processes | The problem of time-series clustering is considered in the case where each data-point is a sample generated by a piecewise stationary ergodic process. Stationary processes are perhaps the most general class of processes considered in non-parametric statistics and allow for arbitrary long-range dependence between variab... | ['Azadeh Khaleghi', 'Daniil Ryabko'] | 2019-06-26 | null | null | null | null | ['time-series-clustering'] | ['time-series'] | [ 1.25088662e-01 -2.82368720e-01 -1.78917661e-01 -2.72195399e-01
-5.39052546e-01 -6.97523117e-01 7.95130432e-01 4.41267043e-01
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-4.91477460e-01 -5.19592643e-01 -4.27524090e-01 -1.22388840e+00
-4.92283911e-01 1.03348815e+00 1.40093014e-01 2.41225004... | [7.064774513244629, 3.892709970474243] |
f71667f4-5e34-4630-99cb-dcaa6c59f235 | semi-automatic-construction-of-word-formation | null | null | https://aclanthology.org/L18-1291 | https://aclanthology.org/L18-1291.pdf | Semi-Automatic Construction of Word-Formation Networks (for Polish and Spanish) | null | ["Zden{\\v{e}}k {\\v{Z}}abokrtsk{\\'y}", "Magda {\\v{S}}ev{\\v{c}}{\\'\\i}kov{\\'a}", 'Mateusz Lango'] | 2018-05-01 | semi-automatic-construction-of-word-formation-1 | https://aclanthology.org/L18-1291 | https://aclanthology.org/L18-1291.pdf | lrec-2018-5 | ['sequential-pattern-mining'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
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-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.27558708190918, 3.759993076324463] |
987cca09-3024-4333-8c2a-993d2b1fcd27 | twist-two-way-inter-label-self-training-for | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Chu_TWIST_Two-Way_Inter-Label_Self-Training_for_Semi-Supervised_3D_Instance_Segmentation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Chu_TWIST_Two-Way_Inter-Label_Self-Training_for_Semi-Supervised_3D_Instance_Segmentation_CVPR_2022_paper.pdf | TWIST: Two-Way Inter-Label Self-Training for Semi-Supervised 3D Instance Segmentation | We explore the way to alleviate the label-hungry problem in a semi-supervised setting for 3D instance segmentation. To leverage the unlabeled data to boost model performance, we present a novel Two-Way Inter-label Self-Training framework named TWIST. It exploits inherent correlations between semantic understanding ... | ['Jiaya Jia', 'Chi-Wing Fu', 'Xiaojuan Qi', 'Xiao Tan', 'Zhengzhe Liu', 'Xiaoqing Ye', 'Ruihang Chu'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['3d-instance-segmentation-1'] | ['computer-vision'] | [ 3.81183088e-01 5.11381865e-01 -5.82379460e-01 -8.98170650e-01
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1.12678774e-01 6.10740483e-01 3.49828869e-01 1.16994306... | [9.6158447265625, 0.7287827134132385] |
f9eb71d5-c695-484b-a8ca-ce413f659baf | detect-localize-repair-a-unified-framework | 2211.14875 | null | https://arxiv.org/abs/2211.14875v3 | https://arxiv.org/pdf/2211.14875v3.pdf | Detect-Localize-Repair: A Unified Framework for Learning to Debug with CodeT5 | Automated software debugging is a crucial task for improving the productivity of software developers. Many neural-based techniques have been proven effective for debugging-related tasks such as bug localization and program repair (or bug fixing). However, these techniques often focus only on either one of them or appro... | ['Steven Hoi', 'Yue Wang', 'Nghi D. Q. Bui'] | 2022-11-27 | null | null | null | null | ['program-repair', 'program-repair'] | ['computer-code', 'reasoning'] | [-1.94198146e-01 -1.60181627e-01 -2.32735455e-01 -4.54189122e-01
-9.53210354e-01 -4.84067172e-01 -2.50571549e-01 4.18539286e-01
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-2.16720700e-01 -1.70349702e-01 2.50764787e-02 2.33574256... | [7.599259853363037, 7.7202863693237305] |
502e8940-2f21-4827-989f-474e58d26d07 | endurance-aware-mapping-of-spiking-neural | 2103.05707 | null | https://arxiv.org/abs/2103.05707v1 | https://arxiv.org/pdf/2103.05707v1.pdf | Endurance-Aware Mapping of Spiking Neural Networks to Neuromorphic Hardware | Neuromorphic computing systems are embracing memristors to implement high density and low power synaptic storage as crossbar arrays in hardware. These systems are energy efficient in executing Spiking Neural Networks (SNNs). We observe that long bitlines and wordlines in a memristive crossbar are a major source of para... | ['Francky Catthoor', 'Nagarajan Kandasamy', 'Nikil Dutt', 'Jeffrey Krichmar', 'Anup Das', 'Shihao Song', 'Twisha Titirsha'] | 2021-03-09 | null | null | null | null | ['graph-partitioning'] | ['graphs'] | [-6.90783486e-02 -4.00793314e-01 -8.17577690e-02 2.41550818e-01
4.29651469e-01 -3.48900080e-01 3.69507894e-02 1.32736906e-01
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-1.52454212e-01 -1.05516231e+00 -1.08111703e+00 -1.07621467e+00
5.44857383e-02 4.14928705e-01 1.11121559e+00 -1.50733471... | [8.219243049621582, 2.5034701824188232] |
d66e91a2-7323-4390-ab35-16033edc5734 | visualizing-ensemble-predictions-of-music | 2112.07627 | null | https://arxiv.org/abs/2112.07627v2 | https://arxiv.org/pdf/2112.07627v2.pdf | Visualizing Ensemble Predictions of Music Mood | Music mood classification has been a challenging problem in comparison with other music classification problems (e.g., genre, composer, or period). One solution for addressing this challenge is to use an ensemble of machine learning models. In this paper, we show that visualization techniques can effectively convey the... | ['Min Chen', 'Zelin Ye'] | 2021-12-14 | null | null | null | null | ['music-classification'] | ['music'] | [-7.85838664e-02 -3.52348745e-01 1.34891301e-01 -1.90342274e-02
-5.24052918e-01 -9.59908485e-01 4.06392097e-01 5.46137154e-01
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-2.22444683e-01 1.57319412e-01 -1.96003765e-01 -2.22453266... | [15.963419914245605, 5.3220391273498535] |
cc18bcf8-5b33-481a-bed1-ec14c3278b7f | hybrid-energy-based-model-in-the-feature | 2305.16966 | null | https://arxiv.org/abs/2305.16966v3 | https://arxiv.org/pdf/2305.16966v3.pdf | Hybrid Energy Based Model in the Feature Space for Out-of-Distribution Detection | Out-of-distribution (OOD) detection is a critical requirement for the deployment of deep neural networks. This paper introduces the HEAT model, a new post-hoc OOD detection method estimating the density of in-distribution (ID) samples using hybrid energy-based models (EBM) in the feature space of a pre-trained backbone... | ['Nicolas Thome', 'Clément Rambour', 'Elias Ramzi', 'Marc Lafon'] | 2023-05-26 | null | null | null | null | ['density-estimation'] | ['methodology'] | [-4.81538624e-01 -4.11519855e-02 -2.14710325e-01 -4.09934402e-01
-9.02282655e-01 -3.51413012e-01 6.88259900e-01 -2.63669282e-01
-2.90732384e-01 4.15108681e-01 -6.07524998e-02 -1.61361143e-01
3.34229916e-01 -5.81070185e-01 -8.95156384e-01 -8.06702852e-01
-2.31148660e-01 4.80396479e-01 1.53743058e-01 4.02574539... | [7.5902910232543945, 3.6289761066436768] |
76c0ea7f-b5fb-4652-b6dc-7b9c32a15039 | ldp-net-an-unsupervised-pansharpening-network | 2111.12483 | null | https://arxiv.org/abs/2111.12483v1 | https://arxiv.org/pdf/2111.12483v1.pdf | LDP-Net: An Unsupervised Pansharpening Network Based on Learnable Degradation Processes | Pansharpening in remote sensing image aims at acquiring a high-resolution multispectral (HRMS) image directly by fusing a low-resolution multispectral (LRMS) image with a panchromatic (PAN) image. The main concern is how to effectively combine the rich spectral information of LRMS image with the abundant spatial inform... | ['Yi Zhang', 'Leyuan Fang', 'Jiliu Zhou', 'Mingzheng Hou', 'Zhongzhou Zhang', 'Zhimin Shao', 'Jiahui Ni'] | 2021-11-24 | null | null | null | null | ['pansharpening'] | ['computer-vision'] | [ 7.41898417e-01 -5.74754000e-01 6.12198301e-02 -2.47132644e-01
-7.89036095e-01 -3.53276432e-01 3.53765696e-01 -2.38180041e-01
-4.18268383e-01 6.02689505e-01 -1.17510751e-01 -1.14290059e-01
-3.76365036e-01 -1.12708318e+00 -4.62970555e-01 -1.18268359e+00
4.70978826e-01 -2.87820309e-01 2.50177085e-01 -2.45510235... | [10.177998542785645, -1.9597004652023315] |
b38436da-2cff-49fb-b59d-193a3bb336e9 | an-experimental-review-of-speaker-diarization | 2305.18074 | null | https://arxiv.org/abs/2305.18074v1 | https://arxiv.org/pdf/2305.18074v1.pdf | An Experimental Review of Speaker Diarization methods with application to Two-Speaker Conversational Telephone Speech recordings | We performed an experimental review of current diarization systems for the conversational telephone speech (CTS) domain. In detail, we considered a total of eight different algorithms belonging to clustering-based, end-to-end neural diarization (EEND), and speech separation guided diarization (SSGD) paradigms. We studi... | ['Stefano Squartini', 'Alessio Brutti', 'Enrico Zovato', 'Giovanni Morrone', 'Samuele Cornell', 'Luca Serafini'] | 2023-05-29 | null | null | null | null | ['speech-separation', 'speaker-diarization'] | ['speech', 'speech'] | [ 2.92317290e-02 4.58589159e-02 1.17327526e-01 -4.96527880e-01
-9.32785511e-01 -4.84001487e-01 6.85511768e-01 -1.91320300e-01
-3.89578760e-01 4.67768699e-01 3.45411092e-01 -6.44236386e-01
-3.69078487e-01 -2.88674265e-01 1.13915056e-02 -8.93820226e-01
1.03759490e-01 1.09308434e+00 1.30325958e-01 -1.62732348... | [14.613312721252441, 6.255340576171875] |
b4da51e5-6e46-4875-b500-d87c274fbee5 | interpretable-network-structure-for-modeling | null | null | https://openreview.net/forum?id=BkgUB1SYPS | https://openreview.net/pdf?id=BkgUB1SYPS | Interpretable Network Structure for Modeling Contextual Dependency | Neural language models have achieved great success in many NLP tasks, to a large extent, due to the ability to capture contextual dependencies among terms in a text. While many efforts have been devoted to empirically explain the connection between the network hyperparameters and the ability to represent the contextual... | ['Ming Zhou.', 'Yuexian Hou', 'Nan Duan', 'Yehua Zhang', 'Xiaoliu Mao', 'Peng Zhang', 'Xindian Ma'] | 2019-09-25 | null | null | null | null | ['sentence-classification'] | ['natural-language-processing'] | [-5.48271127e-02 -2.91182578e-01 -3.44380915e-01 -4.55680400e-01
1.16653256e-01 -1.86111853e-01 3.56811970e-01 1.29939755e-02
-3.02999973e-01 2.76303798e-01 4.84765172e-01 -6.21619880e-01
-3.43217790e-01 -7.61870086e-01 -4.19232637e-01 -8.29974294e-01
7.21532777e-02 1.93730555e-02 9.68088135e-02 -3.43782872... | [10.508101463317871, 9.128493309020996] |
a6e93f80-f23d-4241-8599-196358b6e6dd | self-supervised-domain-adaptation-for-1 | 2107.09372 | null | https://arxiv.org/abs/2107.09372v1 | https://arxiv.org/pdf/2107.09372v1.pdf | Self-Supervised Domain Adaptation for Diabetic Retinopathy Grading using Vessel Image Reconstruction | This paper investigates the problem of domain adaptation for diabetic retinopathy (DR) grading. We learn invariant target-domain features by defining a novel self-supervised task based on retinal vessel image reconstructions, inspired by medical domain knowledge. Then, a benchmark of current state-of-the-art unsupervis... | ['Daniel Sonntag', 'Alexander Prange', 'Ngoc T. T. Than', 'Truong T. N. Mai', 'Duy M. H. Nguyen'] | 2021-07-20 | null | null | null | null | ['diabetic-retinopathy-grading'] | ['medical'] | [ 3.97911191e-01 4.58985060e-01 -3.94846916e-01 -7.78890967e-01
-8.04199219e-01 -3.42806429e-01 4.65075493e-01 -1.56350806e-03
-5.94907463e-01 1.11472201e+00 2.84353197e-01 -1.86744153e-01
-4.35052842e-01 -5.93312979e-01 -2.71949738e-01 -7.03772962e-01
1.33065850e-01 6.70259237e-01 3.29852939e-01 -1.51015937... | [15.774700164794922, -3.937795877456665] |
192fca71-e4ce-457c-a81c-a519da8759ca | facetoponet-facial-expression-recognition | 2209.06322 | null | https://arxiv.org/abs/2209.06322v1 | https://arxiv.org/pdf/2209.06322v1.pdf | FaceTopoNet: Facial Expression Recognition using Face Topology Learning | Prior work has shown that the order in which different components of the face are learned using a sequential learner can play an important role in the performance of facial expression recognition systems. We propose FaceTopoNet, an end-to-end deep model for facial expression recognition, which is capable of learning an... | ['Ali Etemad', 'Alireza Sepas-Moghaddam', 'Mojtaba Kolahdouzi'] | 2022-09-13 | null | null | null | null | ['facial-expression-recognition'] | ['computer-vision'] | [-3.58640142e-02 -1.99322417e-01 -1.25096008e-01 -7.41785944e-01
-4.01349723e-01 -6.59735277e-02 5.48045516e-01 -3.43275279e-01
-2.72759885e-01 2.17064157e-01 1.70864448e-01 2.22218230e-01
2.29978651e-01 -5.18342853e-01 -6.37592793e-01 -8.91945601e-01
-2.60126859e-01 1.45989373e-01 -1.43318981e-01 -2.90394455... | [13.595128059387207, 1.6415380239486694] |
4879973c-eceb-4556-9b07-1f09eaa8c320 | lexicon-learning-for-few-shot-sequence | null | null | https://aclanthology.org/2021.acl-long.382 | https://aclanthology.org/2021.acl-long.382.pdf | Lexicon Learning for Few Shot Sequence Modeling | Sequence-to-sequence transduction is the core problem in language processing applications as diverse as semantic parsing, machine translation, and instruction following. The neural network models that provide the dominant solution to these problems are brittle, especially in low-resource settings: they fail to generali... | ['Jacob Andreas', 'Ekin Akyurek'] | 2021-08-01 | null | null | null | acl-2021-5 | ['systematic-generalization'] | ['reasoning'] | [ 5.48404455e-01 2.15007231e-01 -5.31161666e-01 -4.67637420e-01
-7.70038426e-01 -7.59031892e-01 6.09811604e-01 1.38690680e-01
-5.30582786e-01 9.78843689e-01 4.47344363e-01 -1.15881324e+00
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3.62614036e-01 6.55075908e-01 1.51262641e-01 -5.32325625... | [10.691649436950684, 9.120824813842773] |
34b3854c-9fb8-4328-9004-cc166e891efa | video-coding-for-machine-compact-visual | 2110.09241 | null | https://arxiv.org/abs/2110.09241v1 | https://arxiv.org/pdf/2110.09241v1.pdf | Video Coding for Machine: Compact Visual Representation Compression for Intelligent Collaborative Analytics | Video Coding for Machines (VCM) is committed to bridging to an extent separate research tracks of video/image compression and feature compression, and attempts to optimize compactness and efficiency jointly from a unified perspective of high accuracy machine vision and full fidelity human vision. In this paper, we summ... | ['Jiaying Liu', 'Ling-Yu Duan', 'Yueyu Hu', 'Haofeng Huang', 'Wenhan Yang'] | 2021-10-18 | null | null | null | null | ['feature-compression'] | ['computer-vision'] | [ 7.3097157e-01 1.2976253e-01 -2.5129807e-01 -1.7295535e-01
-4.5177025e-01 -1.6750449e-01 7.1152371e-01 1.3926560e-01
-3.6451322e-01 2.2155853e-01 1.7127620e-01 -7.6364294e-02
-4.5087793e-01 -5.8708978e-01 -5.1690286e-01 -5.0420529e-01
-2.0089133e-01 2.4457317e-02 -1.1345281e-01 1.0241328e-01
5.5445617e-01... | [11.267890930175781, -1.5425772666931152] |
d6533c01-a698-490b-bcc0-6ccb6911d367 | nonnegative-opls-for-supervised-design-of | 2112.12280 | null | https://arxiv.org/abs/2112.12280v1 | https://arxiv.org/pdf/2112.12280v1.pdf | Nonnegative OPLS for Supervised Design of Filter Banks: Application to Image and Audio Feature Extraction | Audio or visual data analysis tasks usually have to deal with high-dimensional and nonnegative signals. However, most data analysis methods suffer from overfitting and numerical problems when data have more than a few dimensions needing a dimensionality reduction preprocessing. Moreover, interpretability about how and ... | ['Vanessa Gómez-Verdejo', 'Jerónimo Arenas García', 'Sergio Muñoz-Romero'] | 2021-12-22 | null | null | null | null | ['genre-classification'] | ['computer-vision'] | [ 1.88954592e-01 -3.61593187e-01 7.56951123e-02 -1.93973064e-01
-1.29087105e-01 -5.58721364e-01 1.47710800e-01 -9.39613767e-03
-4.04866010e-01 6.03838980e-01 1.28902972e-01 -2.13445015e-02
-7.31180370e-01 -6.10477567e-01 -1.73259959e-01 -9.35973823e-01
1.37941852e-01 1.31648496e-01 -1.97347224e-01 -2.81466782... | [12.387856483459473, 0.6351650953292847] |
b1bd20fe-c928-4eb2-9676-3328f65d5a86 | few-shot-object-detection-via-variational | 2301.13411 | null | https://arxiv.org/abs/2301.13411v1 | https://arxiv.org/pdf/2301.13411v1.pdf | Few-Shot Object Detection via Variational Feature Aggregation | As few-shot object detectors are often trained with abundant base samples and fine-tuned on few-shot novel examples,the learned models are usually biased to base classes and sensitive to the variance of novel examples. To address this issue, we propose a meta-learning framework with two novel feature aggregation scheme... | ['Gui-Song Xia', 'Ke Yan', 'Jian Ding', 'Yuqiang Ren', 'Jiaming Han'] | 2023-01-31 | null | null | null | null | ['few-shot-object-detection'] | ['computer-vision'] | [-7.88176805e-02 -2.01428428e-01 -3.10623616e-01 -5.19176483e-01
-1.15540934e+00 -3.91799808e-01 7.60716558e-01 1.93432078e-01
-2.50832498e-01 5.81791699e-01 -4.84403037e-02 3.93279374e-01
-1.06257841e-01 -8.99052143e-01 -8.10076475e-01 -8.17782521e-01
2.13278025e-01 3.33435625e-01 6.00722730e-01 -6.70327693... | [9.794238090515137, 2.381481170654297] |
46dde1a3-1e28-439e-a70c-95616043dc6a | hallucinet-ing-spatiotemporal-representations | 1912.04430 | null | https://arxiv.org/abs/1912.04430v3 | https://arxiv.org/pdf/1912.04430v3.pdf | HalluciNet-ing Spatiotemporal Representations Using a 2D-CNN | Spatiotemporal representations learned using 3D convolutional neural networks (CNN) are currently used in state-of-the-art approaches for action related tasks. However, 3D-CNN are notorious for being memory and compute resource intensive as compared with more simple 2D-CNN architectures. We propose to hallucinate spati... | ['Paritosh Parmar', 'Brendan Morris'] | 2019-12-10 | null | null | null | null | ['action-quality-assessment', 'action-recognition-in-still-images', 'action-anticipation', 'scene-recognition', 'fine-grained-action-recognition'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 3.30999196e-02 1.38330698e-01 -4.04606313e-01 7.40299076e-02
-3.26358318e-01 -2.74992406e-01 5.99012196e-01 -1.05431885e-01
-2.86845088e-01 4.50396597e-01 4.95888025e-01 -1.25870019e-01
2.04006523e-01 -6.63759410e-01 -4.92500275e-01 -5.66699326e-01
-4.19393592e-02 2.32150704e-01 3.05991203e-01 -9.56306309... | [8.337058067321777, 0.6803867220878601] |
27f19269-743d-4798-97ee-ded0c4ac2846 | domain-aware-triplet-loss-in-domain | 2303.01233 | null | https://arxiv.org/abs/2303.01233v1 | https://arxiv.org/pdf/2303.01233v1.pdf | Domain-aware Triplet loss in Domain Generalization | Despite much progress being made in the field of object recognition with the advances of deep learning, there are still several factors negatively affecting the performance of deep learning models. Domain shift is one of these factors and is caused by discrepancies in the distributions of the testing and training data.... | ['Brian Lovell', 'Kaiyu Guo'] | 2023-03-01 | null | null | null | null | ['metric-learning', 'object-recognition', 'metric-learning'] | ['computer-vision', 'computer-vision', 'methodology'] | [-3.29732858e-02 -2.29569808e-01 -2.96071619e-01 -6.72663629e-01
-5.59449375e-01 -5.07596433e-01 2.63126612e-01 -9.79196653e-02
-3.72786790e-01 6.91170394e-01 2.17935681e-01 -5.47041669e-02
-5.38459778e-01 -6.39129162e-01 -5.22423029e-01 -9.05052662e-01
1.42541528e-01 3.95559788e-01 1.51669040e-01 -7.31673837... | [10.201225280761719, 3.1018683910369873] |
71febecf-a6c5-45c2-8abb-431ffef2b22e | learning-to-reach-goals-without-reinforcement-1 | 1912.06088 | null | https://arxiv.org/abs/1912.06088v4 | https://arxiv.org/pdf/1912.06088v4.pdf | Learning to Reach Goals via Iterated Supervised Learning | Current reinforcement learning (RL) algorithms can be brittle and difficult to use, especially when learning goal-reaching behaviors from sparse rewards. Although supervised imitation learning provides a simple and stable alternative, it requires access to demonstrations from a human supervisor. In this paper, we study... | ['Dibya Ghosh', 'Coline Devin', 'Sergey Levine', 'Benjamin Eysenbach', 'Ashwin Reddy', 'Abhishek Gupta', 'Justin Fu'] | 2019-12-12 | null | https://openreview.net/forum?id=rALA0Xo6yNJ | https://openreview.net/pdf?id=rALA0Xo6yNJ | iclr-2021-1 | ['multi-goal-reinforcement-learning'] | ['methodology'] | [ 1.13091469e-01 2.03752071e-01 -5.86648941e-01 -3.44660990e-02
-8.93574715e-01 -7.86088824e-01 6.45650804e-01 7.79547319e-02
-6.96298599e-01 1.23546410e+00 3.55356224e-02 -3.78213733e-01
-2.29656056e-01 -3.85392517e-01 -9.02032971e-01 -8.93838763e-01
-4.12401408e-01 4.41801578e-01 5.51901050e-02 -1.99192762... | [4.239220142364502, 1.5589128732681274] |
f92bb73f-8ad2-4cb0-9161-fa4cddcdc2e7 | improve-sinhala-speech-recognition-through | null | null | https://aclanthology.org/2021.icon-main.26 | https://aclanthology.org/2021.icon-main.26.pdf | Improve Sinhala Speech Recognition Through e2e LF-MMI Model | Automatic speech recognition (ASR) has experienced several paradigm shifts over the years from template-based approaches and statistical modeling to the popular GMM-HMM approach and then to deep learning hybrid model DNN-HMM. The latest shift is to end-to-end (e2e) DNN architecture. We present a study to build an e2e A... | ['Ruwan Weerasinghe', 'Thilini Nadungodage', 'Randil Pushpananda', 'Buddhi Gamage'] | null | null | null | null | icon-2021-12 | ['speech-to-text-translation'] | ['natural-language-processing'] | [ 1.01767238e-02 2.54746705e-01 1.82120547e-01 -4.17942852e-01
-1.35156369e+00 -3.20490927e-01 6.63227499e-01 -4.03353542e-01
-7.29170024e-01 6.46169901e-01 5.45402110e-01 -9.29793894e-01
4.46837544e-01 -3.78313690e-01 -4.18493181e-01 -4.41503823e-01
3.41648251e-01 9.23733711e-01 1.66384473e-01 -5.83831549... | [14.34303092956543, 7.047705173492432] |
9ef8060a-d857-445e-bd69-9388fc829aec | good-for-misconceived-reasons-an-empirical | 2105.14462 | null | https://arxiv.org/abs/2105.14462v1 | https://arxiv.org/pdf/2105.14462v1.pdf | Good for Misconceived Reasons: An Empirical Revisiting on the Need for Visual Context in Multimodal Machine Translation | A neural multimodal machine translation (MMT) system is one that aims to perform better translation by extending conventional text-only translation models with multimodal information. Many recent studies report improvements when equipping their models with the multimodal module, despite the controversy of whether such ... | ['Ben Kao', 'Xiang Li', 'Wei Bi', 'Lingpeng Kong', 'Zhiyong Wu'] | 2021-05-30 | null | https://aclanthology.org/2021.acl-long.480 | https://aclanthology.org/2021.acl-long.480.pdf | acl-2021-5 | ['multimodal-machine-translation'] | ['natural-language-processing'] | [ 4.19262201e-01 4.53858346e-01 -5.79316974e-01 -3.12559456e-01
-1.19844639e+00 -6.87792897e-01 9.86178696e-01 -1.39949664e-01
-2.76611209e-01 7.92705059e-01 7.17011690e-01 -6.76721454e-01
3.39893438e-02 -2.46931046e-01 -8.71477008e-01 -2.80617118e-01
3.19231719e-01 8.98296058e-01 -6.31275356e-01 -6.42638206... | [11.454014778137207, 1.4890210628509521] |
426caf2c-f1a1-40d2-9f57-72a267e4ec55 | data-driven-intelligent-computational-design | 2301.12382 | null | https://arxiv.org/abs/2301.12382v2 | https://arxiv.org/pdf/2301.12382v2.pdf | Data-driven intelligent computational design for products: Method, techniques, and applications | Data-driven intelligent computational design (DICD) is a research hotspot emerged under the context of fast-developing artificial intelligence. It emphasizes on utilizing deep learning algorithms to extract and represent the design features hidden in historical or fabricated design process data, and then learn the comb... | ['Yuhao Liu', 'Tianshuo Zang', 'Pingyu Jiang', 'Maolin Yang'] | 2023-01-29 | null | null | null | null | ['feature-engineering'] | ['methodology'] | [-1.27291277e-01 -4.94598508e-01 -1.14982851e-01 -3.71586531e-01
1.16395364e-02 -2.88379490e-01 3.76008838e-01 9.36024860e-02
4.29934949e-01 1.80520847e-01 2.26262450e-01 -3.23655307e-01
-1.08201742e+00 -1.11587584e+00 -1.20419012e-02 -6.12096667e-01
1.64648280e-01 7.73981929e-01 -6.77529931e-01 -2.27083489... | [5.936655521392822, 3.169497489929199] |
742cb093-ae6e-4ae4-9365-e4121e54b31e | neural-architecture-search-for-joint-human | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Zeng_Neural_Architecture_Search_for_Joint_Human_Parsing_and_Pose_Estimation_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Zeng_Neural_Architecture_Search_for_Joint_Human_Parsing_and_Pose_Estimation_ICCV_2021_paper.pdf | Neural Architecture Search for Joint Human Parsing and Pose Estimation | Human parsing and pose estimation are crucial for the understanding of human behaviors. Since these tasks are closely related, employing one unified model to perform two tasks simultaneously allows them to benefit from each other. However, since human parsing is a pixel-wise classification process while pose estima... | ['Wu Liu', 'Chi Su', 'Junjie Zhang', 'Qian Bao', 'Yuhang Huang', 'Dan Zeng'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['human-parsing'] | ['computer-vision'] | [ 2.98912168e-01 1.61258608e-01 -3.03846262e-02 -6.14803135e-01
-7.80986607e-01 -8.19423497e-02 1.65052578e-01 -1.13754533e-01
-6.72127903e-01 4.16514754e-01 -7.32935518e-02 1.15800552e-01
-9.43094939e-02 -5.31181276e-01 -8.18288565e-01 -6.44724607e-01
-1.03519395e-01 3.79893988e-01 4.21983421e-01 3.12990993... | [7.510929584503174, -0.38988006114959717] |
8421872a-aa79-4f26-849a-1c3142296441 | towards-nonlinear-motion-aware-and-occlusion | 2303.18125 | null | https://arxiv.org/abs/2303.18125v2 | https://arxiv.org/pdf/2303.18125v2.pdf | Towards Nonlinear-Motion-Aware and Occlusion-Robust Rolling Shutter Correction | This paper addresses the problem of rolling shutter correction in complex nonlinear and dynamic scenes with extreme occlusion. Existing methods suffer from two main drawbacks. Firstly, they face challenges in estimating the accurate correction field due to the uniform velocity assumption, leading to significant image c... | ['Xuelong Li', 'Bin Zhao', 'Dong Wang', 'Zhigang Wang', 'Yizhen Lao', 'Delin Qu'] | 2023-03-31 | null | null | null | null | ['unrolling'] | ['computer-vision'] | [ 3.44822466e-01 -7.47558355e-01 5.40760793e-02 -1.84138328e-01
-7.82283545e-01 -3.22512358e-01 1.57620400e-01 -4.98361766e-01
-4.46170986e-01 6.08059585e-01 1.01386584e-01 -6.17617704e-02
1.29976481e-01 -3.03195059e-01 -7.88329244e-01 -6.41823471e-01
2.29683191e-01 -1.70864299e-01 5.16367733e-01 -1.66968629... | [10.780856132507324, -1.8073160648345947] |
e89898f6-e83b-4940-82f9-b14536a66625 | transfer-learning-for-underrepresented-music | 2306.00281 | null | https://arxiv.org/abs/2306.00281v1 | https://arxiv.org/pdf/2306.00281v1.pdf | Transfer Learning for Underrepresented Music Generation | This paper investigates a combinational creativity approach to transfer learning to improve the performance of deep neural network-based models for music generation on out-of-distribution (OOD) genres. We identify Iranian folk music as an example of such an OOD genre for MusicVAE, a large generative music model. We fin... | ['Matthew Guzdial', 'Anahita Doosti'] | 2023-06-01 | null | null | null | null | ['music-generation', 'music-generation'] | ['audio', 'music'] | [ 1.12856366e-01 1.44507438e-01 5.25746532e-02 1.66175336e-01
-8.77449632e-01 -8.44695389e-01 5.20535827e-01 -7.24189222e-01
2.75892951e-02 7.37534285e-01 5.59154451e-01 3.43524367e-02
-4.93507326e-01 -1.01388729e+00 -8.46085846e-01 -3.81557792e-01
9.30731650e-03 1.08895743e+00 -6.31925464e-01 -4.43265885... | [16.040964126586914, 5.508218765258789] |
710f60b1-02f1-4dd4-8f6b-f38ff982d8c2 | generalized-nonconvex-approach-for-low-tubal | null | null | https://ieeexplore.ieee.org/abstract/document/9340243 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9340243 | Generalized Nonconvex Approach for Low-Tubal-Rank Tensor Recovery | The tensor-tensor product-induced tensor nuclear norm (t-TNN) (Lu et al., 2020) minimization for low-tubal-rank tensor recovery attracts broad attention recently. However, minimizing the t-TNN faces some drawbacks. For example, the obtained solution could be suboptimal to the original problem due to its loose approxima... | ['and Xinling Liu', 'Jianwen Huang', 'TingWen Huang', 'Jianjun Wang', 'Feng Zhang', 'Hailin Wang'] | 2022-08-04 | null | null | null | ieee-transactions-on-neural-networks-and-11 | ['image-inpainting', 'low-rank-matrix-completion'] | ['computer-vision', 'methodology'] | [-2.90503204e-01 -2.55593687e-01 -2.56858796e-01 1.40783668e-01
-9.67333019e-01 -3.58480185e-01 -6.32339045e-02 -4.77556586e-01
-2.24911943e-02 6.53380752e-01 6.13426030e-01 4.73433658e-02
-6.92585409e-01 -1.07897520e-01 -6.67000771e-01 -1.00740123e+00
-3.89869571e-01 2.22215861e-01 -1.59961343e-01 -2.95798630... | [7.40500020980835, 4.469921588897705] |
1565700d-f9d2-491a-9cdd-06b916498faa | inference-and-dynamic-decision-making-for | 2209.01092 | null | https://arxiv.org/abs/2209.01092v1 | https://arxiv.org/pdf/2209.01092v1.pdf | Inference and dynamic decision-making for deteriorating systems with probabilistic dependencies through Bayesian networks and deep reinforcement learning | In the context of modern environmental and societal concerns, there is an increasing demand for methods able to identify management strategies for civil engineering systems, minimizing structural failure risks while optimally planning inspection and maintenance (I&M) processes. Most available methods simplify the I&M d... | ['Philippe Rigo', 'Konstantinos G. Papakonstantinou', 'Charalampos P. Andriotis', 'Pablo G. Morato'] | 2022-09-02 | null | null | null | null | ['decision-making-under-uncertainty', 'decision-making-under-uncertainty'] | ['medical', 'reasoning'] | [-2.69032884e-02 4.29920524e-01 -1.28737697e-02 1.50930002e-01
-4.06331599e-01 -8.47890601e-02 2.11526558e-01 3.57978046e-01
-1.66447297e-01 8.95424306e-01 -1.74791873e-01 -2.11292580e-01
-9.94452000e-01 -7.72464573e-01 -4.62208599e-01 -1.14003634e+00
-2.07303450e-01 8.91292274e-01 -1.41433533e-02 -2.80761093... | [4.679698944091797, 2.3638508319854736] |
d6172700-3864-4861-904f-88c70ef74a4b | exploiting-network-structures-to-improve | 2107.05885 | null | https://arxiv.org/abs/2107.05885v1 | https://arxiv.org/pdf/2107.05885v1.pdf | Exploiting Network Structures to Improve Semantic Representation for the Financial Domain | This paper presents the participation of the MiniTrue team in the FinSim-3 shared task on learning semantic similarities for the financial domain in English language. Our approach combines contextual embeddings learned by transformer-based language models with network structures embeddings extracted on external knowled... | ['Shi-jie We', 'Chao Feng'] | 2021-07-13 | null | https://aclanthology.org/2021.finnlp-1.10 | https://aclanthology.org/2021.finnlp-1.10.pdf | finnlp-2021-8 | ['knowledge-graph-embeddings', 'knowledge-graph-embeddings'] | ['graphs', 'methodology'] | [-7.57871509e-01 5.39276898e-01 -3.59015465e-01 -3.99488032e-01
-1.19360924e-01 -3.91811758e-01 1.01446903e+00 2.69661605e-01
-5.49360991e-01 5.17554283e-01 5.27612746e-01 -3.21590990e-01
-2.17125818e-01 -1.16933477e+00 -5.22491097e-01 4.46833558e-02
-7.71508813e-02 6.18786335e-01 4.78767127e-01 -4.35548156... | [9.419897079467773, 8.268871307373047] |
9b159b48-cad0-4af4-a95d-0199c718832c | set-prediction-without-imposing-structure-as-1 | 2010.04109 | null | https://arxiv.org/abs/2010.04109v2 | https://arxiv.org/pdf/2010.04109v2.pdf | Set Prediction without Imposing Structure as Conditional Density Estimation | Set prediction is about learning to predict a collection of unordered variables with unknown interrelations. Training such models with set losses imposes the structure of a metric space over sets. We focus on stochastic and underdefined cases, where an incorrectly chosen loss function leads to implausible predictions. ... | ['Cees G. M. Snoek', 'Gertjan J. Burghouts', 'David W. Zhang'] | 2020-10-08 | set-prediction-without-imposing-structure-as | https://openreview.net/forum?id=04ArenGOz3 | https://openreview.net/pdf?id=04ArenGOz3 | iclr-2021-1 | ['point-cloud-reconstruction'] | ['computer-vision'] | [ 6.12563550e-01 2.60087132e-01 -4.50945258e-01 -7.86630988e-01
-1.29314733e+00 -5.29994071e-01 7.73745060e-01 4.41480689e-02
-1.01673238e-01 1.36753118e+00 5.63315861e-02 -3.28147173e-01
-1.65789336e-01 -9.22260225e-01 -1.32567906e+00 -8.41368496e-01
-1.54259196e-02 1.09872770e+00 -1.02724731e-01 2.59202197... | [7.087588787078857, 4.087517738342285] |
3ed49fe0-257c-4c82-a97f-6f31cb00a383 | xricl-cross-lingual-retrieval-augmented-in | 2210.13693 | null | https://arxiv.org/abs/2210.13693v1 | https://arxiv.org/pdf/2210.13693v1.pdf | XRICL: Cross-lingual Retrieval-Augmented In-Context Learning for Cross-lingual Text-to-SQL Semantic Parsing | In-context learning using large language models has recently shown surprising results for semantic parsing tasks such as Text-to-SQL translation. Prompting GPT-3 or Codex using several examples of question-SQL pairs can produce excellent results, comparable to state-of-the-art finetuning-based models. However, existing... | ['Jimmy Lin', 'He Bai', 'Rui Zhang', 'Peng Shi'] | 2022-10-25 | null | null | null | null | ['text-to-sql', 'semantic-parsing'] | ['computer-code', 'natural-language-processing'] | [-2.54926784e-03 1.18939921e-01 -4.22465682e-01 -7.81201720e-01
-1.84296823e+00 -7.99636066e-01 3.64201933e-01 -5.08427136e-02
-4.09714222e-01 6.47046804e-01 3.75859618e-01 -7.87171781e-01
3.26480925e-01 -9.39067721e-01 -1.15210748e+00 4.49525006e-02
6.73748672e-01 9.17078078e-01 2.10648239e-01 -5.29180586... | [10.8541841506958, 8.8656644821167] |
709b76ad-e707-4fa9-9768-f9e22fe907a8 | fast-dynamic-vision-detection-and-tracking | 2103.05903 | null | https://arxiv.org/abs/2103.05903v2 | https://arxiv.org/pdf/2103.05903v2.pdf | FAST-Dynamic-Vision: Detection and Tracking Dynamic Objects with Event and Depth Sensing | The development of aerial autonomy has enabled aerial robots to fly agilely in complex environments. However, dodging fast-moving objects in flight remains a challenge, limiting the further application of unmanned aerial vehicles (UAVs). The bottleneck of solving this problem is the accurate perception of rapid dynamic... | ['Fei Gao', 'Chao Xu', 'Qianli Dong', 'Zhiwei Zhang', 'Dong Wang', 'Siyuan Wu', 'Haojia Li', 'Botao He'] | 2021-03-10 | null | null | null | null | ['robust-object-detection'] | ['computer-vision'] | [ 7.58859888e-02 -7.00291812e-01 7.55807161e-02 -2.43590206e-01
3.78132053e-03 -8.14299524e-01 2.28473514e-01 -1.84692711e-01
-4.11831528e-01 4.61979061e-01 -5.26104093e-01 -1.94764026e-02
-1.76327199e-01 -6.58358574e-01 -3.54690552e-01 -7.41934001e-01
-1.50121987e-01 8.16036761e-02 9.03331518e-01 -2.21257940... | [7.2083821296691895, -1.75824773311615] |
13fee72e-548a-4daa-9638-c971184147bd | explaining-and-adapting-graph-conditional | 2306.03256 | null | https://arxiv.org/abs/2306.03256v1 | https://arxiv.org/pdf/2306.03256v1.pdf | Explaining and Adapting Graph Conditional Shift | Graph Neural Networks (GNNs) have shown remarkable performance on graph-structured data. However, recent empirical studies suggest that GNNs are very susceptible to distribution shift. There is still significant ambiguity about why graph-based models seem more vulnerable to these shifts. In this work we provide a thoro... | ['Bryan Perozzi', 'Jiawei Han', 'Natalia Ponomareva', 'Yizhu Jiao', 'Qi Zhu'] | 2023-06-05 | null | null | null | null | ['graph-classification', 'unsupervised-domain-adaptation'] | ['graphs', 'methodology'] | [ 3.97162169e-01 3.63312215e-01 -3.61642241e-01 -3.56739044e-01
-1.71569034e-01 -5.75145006e-01 5.24222553e-01 5.24544120e-01
-1.49315894e-01 7.89209187e-01 -9.48475674e-02 -5.05038679e-01
-3.34201694e-01 -9.05209303e-01 -7.94123232e-01 -5.20701349e-01
-4.30520177e-01 4.01054740e-01 1.60441384e-01 3.96488905... | [6.91973876953125, 6.119566917419434] |
d291aa4d-900b-4429-8cfc-6dc21ac2d2d4 | siminet-a-novel-method-for-quantifying-brain | 1709.07211 | null | http://arxiv.org/abs/1709.07211v1 | http://arxiv.org/pdf/1709.07211v1.pdf | SimiNet: a Novel Method for Quantifying Brain Network Similarity | Quantifying the similarity between two networks is critical in many
applications. A number of algorithms have been proposed to compute graph
similarity, mainly based on the properties of nodes and edges. Interestingly,
most of these algorithms ignore the physical location of the nodes, which is a
key factor in the cont... | [] | 2017-09-21 | null | null | null | null | ['object-categorization', 'graph-similarity'] | ['computer-vision', 'graphs'] | [-2.59786267e-02 5.38176820e-02 3.70958507e-01 -2.51532048e-01
5.76081514e-01 -6.64986968e-01 7.69016743e-01 7.04199374e-01
-5.37168443e-01 2.14600921e-01 -2.40750298e-01 1.07511980e-02
-6.73506141e-01 -9.84638095e-01 -1.47598639e-01 -3.35280091e-01
-5.04846871e-01 4.22235072e-01 4.99484211e-01 -3.50949526... | [12.25837516784668, 3.414670944213867] |
0fb27f6a-b52a-47a7-8268-e5e8456c4ddb | covid-19-detection-and-analysis-from-lung-ct | 2209.10963 | null | https://arxiv.org/abs/2209.10963v2 | https://arxiv.org/pdf/2209.10963v2.pdf | COVID-19 Detection and Analysis From Lung CT Images using Novel Channel Boosted CNNs | In December 2019, the global pandemic COVID-19 in Wuhan, China, affected human life and the worldwide economy. Therefore, an efficient diagnostic system is required to control its spread. However, the automatic diagnostic system poses challenges with a limited amount of labeled data, minor contrast variation, and high ... | ['Saddam Hussain Khan'] | 2022-09-22 | null | null | null | null | ['covid-19-detection'] | ['medical'] | [ 2.40197256e-01 -4.44976270e-01 -6.04365356e-02 -3.81404683e-02
-7.02194452e-01 -3.69507462e-01 1.55002281e-01 -6.20813668e-02
-4.14459944e-01 7.10407913e-01 -2.34711543e-01 -4.42680985e-01
7.49096125e-02 -8.10905457e-01 -4.71784681e-01 -1.11426520e+00
1.64184370e-04 4.49102223e-01 4.72772330e-01 1.17436327... | [15.513184547424316, -1.7724653482437134] |
ddef22ec-369a-4a46-98d9-6bd3d5457e73 | cocolot-combining-complementary-trackers-in | 2205.04261 | null | https://arxiv.org/abs/2205.04261v1 | https://arxiv.org/pdf/2205.04261v1.pdf | CoCoLoT: Combining Complementary Trackers in Long-Term Visual Tracking | How to combine the complementary capabilities of an ensemble of different algorithms has been of central interest in visual object tracking. A significant progress on such a problem has been achieved, but considering short-term tracking scenarios. Instead, long-term tracking settings have been substantially ignored by ... | ['Christian Micheloni', 'Matteo Dunnhofer'] | 2022-05-09 | null | null | null | null | ['visual-tracking', 'visual-object-tracking'] | ['computer-vision', 'computer-vision'] | [-2.80733198e-01 -3.03135812e-01 -1.77524030e-01 4.21667472e-02
-3.61709774e-01 -9.06135917e-01 8.55142772e-01 -1.30533176e-02
-5.18791258e-01 5.36700487e-01 -3.29204291e-01 -2.49574706e-01
-8.29543769e-02 -1.29280254e-01 -8.11184466e-01 -8.40389669e-01
-1.24786317e-01 4.63486761e-01 9.37921405e-01 -2.27485085... | [6.335813045501709, -2.073403835296631] |
a7183276-f986-403d-af9c-8f3ec30f60d5 | general-purpose-tagging-of-freesound-audio | 1807.09902 | null | http://arxiv.org/abs/1807.09902v3 | http://arxiv.org/pdf/1807.09902v3.pdf | General-purpose Tagging of Freesound Audio with AudioSet Labels: Task Description, Dataset, and Baseline | This paper describes Task 2 of the DCASE 2018 Challenge, titled
"General-purpose audio tagging of Freesound content with AudioSet labels". This
task was hosted on the Kaggle platform as "Freesound General-Purpose Audio
Tagging Challenge". The goal of the task is to build an audio tagging system
that can recognize the c... | ['Xavier Serra', 'Xavier Favory', 'Manoj Plakal', 'Eduardo Fonseca', 'Daniel P. W. Ellis', 'Jordi Pons', 'Frederic Font'] | 2018-07-26 | null | null | null | null | ['audio-tagging'] | ['audio'] | [ 5.42292669e-02 1.43415317e-01 -5.29647507e-02 -3.41168225e-01
-1.75180447e+00 -9.12078559e-01 1.23239145e-01 1.38657046e-02
-2.19386742e-01 4.48287129e-01 8.99112821e-01 4.54962760e-01
2.22899780e-01 -6.55989796e-02 -6.35817230e-01 -3.32735121e-01
-4.42868054e-01 2.56242663e-01 3.22402537e-01 -5.65896370... | [15.232882499694824, 5.064076900482178] |
7741255d-dbf9-4d1e-b2d9-aca451f27133 | why-existing-multimodal-crowd-counting | 2304.06401 | null | https://arxiv.org/abs/2304.06401v1 | https://arxiv.org/pdf/2304.06401v1.pdf | Why Existing Multimodal Crowd Counting Datasets Can Lead to Unfulfilled Expectations in Real-World Applications | More information leads to better decisions and predictions, right? Confirming this hypothesis, several studies concluded that the simultaneous use of optical and thermal images leads to better predictions in crowd counting. However, the way multimodal models extract enriched features from both modalities is not yet ful... | ['Elke Hergenröther', 'Martin Thißen'] | 2023-04-13 | null | null | null | null | ['crowd-counting'] | ['computer-vision'] | [-4.65387031e-02 -2.17621744e-01 5.85154332e-02 -3.12941432e-01
-2.61021584e-01 -5.66042125e-01 8.32643926e-01 3.84713411e-01
-8.66435468e-01 8.17614734e-01 6.71720132e-02 -1.69306949e-01
-9.94443670e-02 -7.39392340e-01 -3.12721789e-01 -7.55416870e-01
3.25842440e-01 5.80894291e-01 4.09991115e-01 -1.89349711... | [12.975988388061523, 5.216159820556641] |
70f3ccf7-a054-49c8-a4d0-cc574431f299 | performance-evaluation-and-hybrid-application | 2302.11740 | null | https://arxiv.org/abs/2302.11740v1 | https://arxiv.org/pdf/2302.11740v1.pdf | Performance Evaluation and Hybrid Application of the Greedy and Predictive UAV Trajectory Optimization Methods for Localizing a Target Mobile Device | This study investigates unmanned aerial vehicle (UAV) trajectory planning strategies for localizing a target mobile device in emergency situations. The global navigation satellite system (GNSS)-based accurate position information of a target mobile device in an emergency may not be always available to first responders.... | ['Jiwon Seo', 'Halim Lee'] | 2023-02-23 | null | null | null | null | ['trajectory-planning'] | ['robots'] | [-2.00746413e-02 -2.59755135e-01 -6.87619224e-02 3.16548347e-01
-3.75414044e-01 -8.18919420e-01 1.45149067e-01 3.31115365e-01
-4.67756808e-01 1.00619805e+00 -5.46041608e-01 -7.71863759e-01
-6.38697326e-01 -8.77315700e-01 -5.16764283e-01 -9.43802238e-01
-3.74080569e-01 8.06744397e-02 1.87227920e-01 -2.97294885... | [6.139701843261719, 1.269937515258789] |
f9fd7ed9-a227-4973-bc30-1c3fd31ee9f3 | pay-attention-to-the-activations-a-modular | 1907.13075 | null | https://arxiv.org/abs/1907.13075v1 | https://arxiv.org/pdf/1907.13075v1.pdf | Pay attention to the activations: a modular attention mechanism for fine-grained image recognition | Fine-grained image recognition is central to many multimedia tasks such as search, retrieval and captioning. Unfortunately, these tasks are still challenging since the appearance of samples of the same class can be more different than those from different classes. Attention has been typically implemented in neural netw... | ['Jordi Gonzàlez Sabaté', 'Josep M. Gonfaus', 'Guillem Cucurull Preixens', 'Pau Rodríguez López', 'F. Xavier Roca Marva', 'Diego Velazquez Dorta'] | 2019-07-30 | null | null | null | null | ['fine-grained-image-recognition'] | ['computer-vision'] | [ 2.58996099e-01 -8.60152021e-02 4.64713143e-04 -3.08976829e-01
-5.95385313e-01 -5.46107531e-01 4.35457438e-01 4.37617376e-02
-7.66053081e-01 6.21829271e-01 -2.15877399e-01 1.08427823e-01
1.95985846e-02 -6.82732463e-01 -1.21847272e+00 -6.16029263e-01
1.06432550e-01 4.08356518e-01 3.62125278e-01 -1.97688162... | [9.467645645141602, 2.1157848834991455] |
66ec84f7-dbc8-4bec-bb2a-d49368da256f | continuous-time-analog-filters-for-audio-edge | 2206.02639 | null | https://arxiv.org/abs/2206.02639v2 | https://arxiv.org/pdf/2206.02639v2.pdf | Continuous-Time Analog Filters for Audio Edge Intelligence: Review on Circuit Designs | Edge audio devices can reduce data bandwidth requirements by pre-processing input speech on the device before transmission to the cloud. As edge devices are required to ensure always-on operation, their stringent power constraints pose several design challenges and force IC designers to look for solutions that use low ... | ['Shih-Chii Liu', 'Kwantae Kim'] | 2022-06-06 | null | null | null | null | ['keyword-spotting'] | ['speech'] | [ 2.96126872e-01 -3.23615134e-01 -1.23698197e-01 -2.25002438e-01
-4.32005286e-01 -7.70922661e-01 -1.64774284e-01 -5.96514121e-02
-2.85012066e-01 3.76726985e-01 1.41549826e-01 -6.56848490e-01
-1.81147605e-01 -4.99648869e-01 -3.27636927e-01 -1.77954897e-01
1.43459663e-01 -2.41013616e-01 1.49997413e-01 -1.02709539... | [14.573966979980469, 5.573640823364258] |
57988744-fb62-42a1-a5ce-3b548c01e59d | explanations-from-large-language-models-make | 2210.06726 | null | https://arxiv.org/abs/2210.06726v1 | https://arxiv.org/pdf/2210.06726v1.pdf | Explanations from Large Language Models Make Small Reasoners Better | Integrating free-text explanations to in-context learning of large language models (LLM) is shown to elicit strong reasoning capabilities along with reasonable explanations. In this paper, we consider the problem of leveraging the explanations generated by LLM to improve the training of small reasoners, which are more ... | ['Xifeng Yan', 'Wenhu Chen', 'Yi Mao', 'Baolin Peng', 'Jing Qian', 'Hong Wang', 'Zekun Li', 'Xinlu Zhang', 'Zhiyu Chen', 'Yelong Shen', 'Jianshu Chen', 'Shiyang Li'] | 2022-10-13 | null | null | null | null | ['explanation-generation'] | ['natural-language-processing'] | [ 3.95597458e-01 1.03880680e+00 -4.29004192e-01 -5.22895992e-01
-1.20093846e+00 -3.78997266e-01 8.60685349e-01 -4.42489348e-02
8.34476575e-02 9.47822392e-01 6.72571719e-01 -9.90231216e-01
-1.09350756e-01 -4.61359382e-01 -1.04532301e+00 9.19408947e-02
1.81604475e-01 9.82311487e-01 -1.55825809e-01 -2.96562225... | [9.61400032043457, 7.002117156982422] |
e860652f-c29c-408e-b6a5-1b0c6211a101 | somtimes-self-organizing-maps-for-time-series | 2108.11523 | null | https://arxiv.org/abs/2108.11523v1 | https://arxiv.org/pdf/2108.11523v1.pdf | SOMTimeS: Self Organizing Maps for Time Series Clustering and its Application to Serious Illness Conversations | There is an increasing demand for scalable algorithms capable of clustering and analyzing large time series datasets. The Kohonen self-organizing map (SOM) is a type of unsupervised artificial neural network for visualizing and clustering complex data, reducing the dimensionality of data, and selecting influential feat... | ['Robert Gramling', 'Byung Suk Lee', 'Donna M. Rizzo', 'Ali Javed'] | 2021-08-26 | null | null | null | null | ['time-series-clustering'] | ['time-series'] | [ 8.04033205e-02 -1.23474620e-01 1.08930461e-01 -3.92476439e-01
-3.20226699e-01 -5.78374207e-01 4.12096083e-01 6.76667035e-01
-6.33413732e-01 1.71262875e-01 4.49787766e-01 -3.67428660e-01
-9.08972740e-01 -7.18278289e-01 2.85884328e-02 -9.39533949e-01
-6.65697515e-01 8.67051780e-01 2.53556609e-01 -4.44099456... | [7.252604007720947, 3.3931961059570312] |
9543a6f3-6cce-4b2a-9a58-9179ea7267d2 | global-bilateral-symmetry-detection-using | null | null | https://hal.archives-ouvertes.fr/ujm-01387193 | https://hal-ujm.archives-ouvertes.fr/ujm-01387193v2/document | Global Bilateral Symmetry Detection Using Multiscale Mirror Histograms | In recent years, there has been renewed interest in bilateral symmetry detection in images. It consists in detecting the main bilateral symmetry axis inside artificial or natural images. State-of-the-art methods combine feature point detection, pairwise comparison and voting in Hough-like space. In spite of their good ... | ['Philippe Colantoni', 'Christophe Ducottet', 'Cécile Barat', 'Mohamed Elawady'] | 2016-10-01 | null | null | null | null | ['symmetry-detection'] | ['computer-vision'] | [ 1.39331609e-01 -5.25050163e-01 5.27717099e-02 -1.39373466e-01
-5.47862589e-01 -5.91975808e-01 7.52179325e-01 3.89102474e-02
-4.54093874e-01 4.56262946e-01 2.54790962e-01 1.17773287e-01
-1.99475124e-01 -6.36775732e-01 -2.99452931e-01 -4.78729963e-01
-9.84972492e-02 2.20468104e-01 8.27487350e-01 -2.10694477... | [8.991484642028809, -2.0091652870178223] |
6f641c33-9374-4c47-acd8-de4342c0ff70 | review-of-face-presentation-attack-detection | 2112.11290 | null | https://arxiv.org/abs/2112.11290v1 | https://arxiv.org/pdf/2112.11290v1.pdf | Review of Face Presentation Attack Detection Competitions | Face presentation attack detection (PAD) has received increasing attention ever since the vulnerabilities to spoofing have been widely recognized. The state of the art in unimodal and multi-modal face anti-spoofing has been assessed in eight international competitions organized in conjunction with major biometrics and ... | ['Guoying Zhao', 'Xiaobai Li', 'Jukka Komulainen', 'Zitong Yu'] | 2021-12-21 | null | null | null | null | ['face-presentation-attack-detection'] | ['computer-vision'] | [ 5.03537118e-01 -3.19205433e-01 6.43800274e-02 -4.05972302e-02
-4.49708939e-01 -7.74941683e-01 7.91867733e-01 -4.05657440e-01
-3.69904786e-01 4.62113649e-01 6.87713698e-02 -1.54887483e-01
-1.56851470e-01 -2.83456445e-01 -2.40157545e-01 -8.03591073e-01
-3.48116159e-01 -7.40625262e-02 2.65848786e-02 -4.10475612... | [13.055747985839844, 1.1148267984390259] |
c1c4ee95-cd92-4996-9458-8f71ae786cc6 | multi-level-and-multi-scale-feature-1 | 1706.06810 | null | http://arxiv.org/abs/1706.06810v1 | http://arxiv.org/pdf/1706.06810v1.pdf | Multi-Level and Multi-Scale Feature Aggregation Using Sample-level Deep Convolutional Neural Networks for Music Classification | Music tag words that describe music audio by text have different levels of
abstraction. Taking this issue into account, we propose a music classification
approach that aggregates multi-level and multi-scale features using pre-trained
feature extractors. In particular, the feature extractors are trained in
sample-level ... | ['Juhan Nam', 'Jongpil Lee'] | 2017-06-21 | null | null | null | null | ['music-classification'] | ['music'] | [ 1.91392899e-01 -4.44014192e-01 -7.26058632e-02 -2.49721348e-01
-1.07996070e+00 -8.29688787e-01 3.54754746e-01 3.54063399e-02
-2.11981729e-01 1.36493832e-01 3.11599523e-01 3.04819793e-01
-4.87644643e-01 -8.34536493e-01 -5.64006090e-01 -4.07853514e-01
-3.71305466e-01 2.01138034e-02 -2.04332937e-02 -9.74577963... | [15.774018287658691, 5.228280067443848] |
5f26f95a-3772-4259-a133-8d67e808952c | pareto-self-supervised-training-for-few-shot | 2104.07841 | null | https://arxiv.org/abs/2104.07841v2 | https://arxiv.org/pdf/2104.07841v2.pdf | Pareto Self-Supervised Training for Few-Shot Learning | While few-shot learning (FSL) aims for rapid generalization to new concepts with little supervision, self-supervised learning (SSL) constructs supervisory signals directly computed from unlabeled data. Exploiting the complementarity of these two manners, few-shot auxiliary learning has recently drawn much attention to ... | ['Donglin Wang', 'Siteng Huang', 'Heshen Zhan', 'Jixie Ge', 'Zhengyu Chen'] | 2021-04-16 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Chen_Pareto_Self-Supervised_Training_for_Few-Shot_Learning_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Chen_Pareto_Self-Supervised_Training_for_Few-Shot_Learning_CVPR_2021_paper.pdf | cvpr-2021-1 | ['auxiliary-learning'] | ['methodology'] | [ 3.55996996e-01 1.11559018e-01 -5.92354178e-01 -4.21837777e-01
-9.99864995e-01 -1.92683965e-01 3.52658927e-01 1.36857972e-01
-4.62049305e-01 8.76100600e-01 1.03214927e-01 1.65871114e-01
-5.44468403e-01 -4.44763571e-01 -5.35748482e-01 -1.05321658e+00
2.36988395e-01 4.96563792e-01 1.97287723e-01 -1.00847617... | [10.05685806274414, 3.2963805198669434] |
aa6de50b-3d68-45eb-b764-3dd25ea7a3f0 | density-open-domain-dialogue-evaluation | 2305.04720 | null | https://arxiv.org/abs/2305.04720v2 | https://arxiv.org/pdf/2305.04720v2.pdf | DEnsity: Open-domain Dialogue Evaluation Metric using Density Estimation | Despite the recent advances in open-domain dialogue systems, building a reliable evaluation metric is still a challenging problem. Recent studies proposed learnable metrics based on classification models trained to distinguish the correct response. However, neural classifiers are known to make overly confident predicti... | ['Seungil Chad Lee', 'Jaegul Choo', 'Daniel Rim', 'ChaeHun Park'] | 2023-05-08 | null | null | null | null | ['dialogue-evaluation'] | ['natural-language-processing'] | [-2.19941005e-01 9.23198611e-02 -2.39631727e-01 -1.05743623e+00
-1.07953894e+00 -5.28706074e-01 6.33449256e-01 2.28415236e-01
-4.65974241e-01 1.18052971e+00 3.94315481e-01 -1.22579344e-01
1.29505217e-01 -7.75715590e-01 -2.21649751e-01 -4.32534218e-01
2.38568857e-01 7.56093800e-01 9.78882611e-02 -1.25121608... | [12.75041675567627, 7.991774082183838] |
a84f3e3c-3131-4a30-96be-ca4c362f3706 | idpl-intra-subdomain-adaptation-adversarial | 2210.03435 | null | https://arxiv.org/abs/2210.03435v2 | https://arxiv.org/pdf/2210.03435v2.pdf | IDPL: Intra-subdomain adaptation adversarial learning segmentation method based on Dynamic Pseudo Labels | Unsupervised domain adaptation(UDA) has been applied to image semantic segmentation to solve the problem of domain offset. However, in some difficult categories with poor recognition accuracy, the segmentation effects are still not ideal. To this end, in this paper, Intra-subdomain adaptation adversarial learning segme... | ['Xuzhou Fu', 'Jie Gao', 'Jian Yu', 'Weilun Zhang', 'XueWei Li'] | 2022-10-07 | null | null | null | null | ['subdomain-adaptation'] | ['methodology'] | [ 4.58174765e-01 2.49712855e-01 -3.85233372e-01 -3.07933360e-01
-5.95014393e-01 -2.69033492e-01 1.07944034e-01 -7.17400983e-02
-3.37328434e-01 5.26270330e-01 -9.03448313e-02 5.48400879e-02
-1.11986808e-01 -9.59829092e-01 -2.42433444e-01 -9.52799857e-01
4.41190511e-01 7.05500901e-01 4.91389781e-01 -7.27303848... | [9.691176414489746, 1.378183364868164] |
15b8d38c-dd79-4407-8eb8-9871c5f41719 | adapting-pretrained-text-to-text-models-for | 2209.10052 | null | https://arxiv.org/abs/2209.10052v2 | https://arxiv.org/pdf/2209.10052v2.pdf | Adapting Pretrained Text-to-Text Models for Long Text Sequences | We present an empirical study of adapting an existing pretrained text-to-text model for long-sequence inputs. Through a comprehensive study along three axes of the pretraining pipeline -- model architecture, optimization objective, and pretraining corpus, we propose an effective recipe to build long-context models from... | ['Wen-tau Yih', 'Yashar Mehdad', 'Shubham Toshniwal', 'Anchit Gupta', 'Wenhan Xiong'] | 2022-09-21 | null | null | null | null | ['long-range-modeling'] | ['natural-language-processing'] | [ 5.07340789e-01 3.07694018e-01 -2.87526786e-01 -4.77767259e-01
-1.44828415e+00 -6.70158744e-01 6.01892829e-01 3.80436182e-02
-5.59986472e-01 9.06982124e-01 8.96620870e-01 -5.23681104e-01
2.28149101e-01 -2.72438705e-01 -9.44363296e-01 -2.59498447e-01
2.51454592e-01 6.78724527e-01 -7.96120837e-02 -3.83939922... | [11.709339141845703, 8.948652267456055] |
5af459c4-1602-40f5-ac80-75d00e03aafa | measuring-your-aste-models-in-the-wild-a | 2305.17448 | null | https://arxiv.org/abs/2305.17448v1 | https://arxiv.org/pdf/2305.17448v1.pdf | Measuring Your ASTE Models in The Wild: A Diversified Multi-domain Dataset For Aspect Sentiment Triplet Extraction | Aspect Sentiment Triplet Extraction (ASTE) is widely used in various applications. However, existing ASTE datasets are limited in their ability to represent real-world scenarios, hindering the advancement of research in this area. In this paper, we introduce a new dataset, named DMASTE, which is manually annotated to b... | ['Xinyu Dai', 'Fei Zhao', 'Jiaze Chen', 'Zhen Wu', 'Huiyun Yang', 'Ting Xu'] | 2023-05-27 | null | null | null | null | ['aspect-sentiment-triplet-extraction'] | ['natural-language-processing'] | [-1.93434089e-01 -5.38437009e-01 -5.27914762e-01 -6.90509915e-01
-7.49794066e-01 -7.99025893e-01 5.13929069e-01 -2.10008353e-01
-2.41510257e-01 6.26750052e-01 2.34418273e-01 -1.93176314e-01
2.30216548e-01 -5.55538595e-01 -1.76681146e-01 -2.79790342e-01
5.16810358e-01 5.22841156e-01 9.05002803e-02 -4.77764010... | [11.41590690612793, 6.712143898010254] |
8f0e16ad-2b32-4a21-95ca-3a66841583f2 | video-dialog-as-conversation-about-objects | 2207.03656 | null | https://arxiv.org/abs/2207.03656v1 | https://arxiv.org/pdf/2207.03656v1.pdf | Video Dialog as Conversation about Objects Living in Space-Time | It would be a technological feat to be able to create a system that can hold a meaningful conversation with humans about what they watch. A setup toward that goal is presented as a video dialog task, where the system is asked to generate natural utterances in response to a question in an ongoing dialog. The task poses ... | ['Truyen Tran', 'Tu Minh Phuong', 'Vuong Le', 'Thao Minh Le', 'Hoang-Anh Pham'] | 2022-07-08 | null | null | null | null | ['video-question-answering', 'visual-dialogue', 'relational-reasoning', 'visual-dialogue'] | ['computer-vision', 'computer-vision', 'natural-language-processing', 'natural-language-processing'] | [ 6.58841804e-02 2.77739912e-01 -1.41811922e-01 -4.79672611e-01
-4.65656608e-01 -6.85513854e-01 1.09099078e+00 8.67303908e-02
-2.42975533e-01 5.11685908e-01 7.43781209e-01 -9.21990350e-02
1.62741944e-01 -6.71750426e-01 -5.12028933e-01 -3.03389817e-01
1.14245519e-01 7.15678453e-01 5.97268760e-01 -5.13731658... | [10.817216873168945, 1.1336114406585693] |
a96405a9-5402-4c94-89f6-be4f69fb5fb1 | openfwi-benchmark-seismic-datasets-for | 2111.02926 | null | https://arxiv.org/abs/2111.02926v6 | https://arxiv.org/pdf/2111.02926v6.pdf | OpenFWI: Large-Scale Multi-Structural Benchmark Datasets for Seismic Full Waveform Inversion | Full waveform inversion (FWI) is widely used in geophysics to reconstruct high-resolution velocity maps from seismic data. The recent success of data-driven FWI methods results in a rapidly increasing demand for open datasets to serve the geophysics community. We present OpenFWI, a collection of large-scale multi-struc... | ['Yinpeng Chen', 'Hanchen Wang', 'Youzuo Lin', 'Qili Zeng', 'Xitong Zhang', 'Peng Jin', 'Shihang Feng', 'Yinan Feng', 'Chengyuan Deng'] | 2021-11-04 | null | null | null | null | ['geophysics'] | ['miscellaneous'] | [-1.42422274e-01 -3.26113909e-01 8.55750591e-02 -3.04047048e-01
-1.24818325e+00 -6.10433638e-01 5.92514873e-01 -1.57320559e-01
-1.12495713e-01 1.02321625e+00 4.86591876e-01 -6.02163970e-01
-2.76819766e-01 -1.20576441e+00 -7.91712582e-01 -1.02050579e+00
-5.79426944e-01 6.14003241e-01 3.36381137e-01 -3.22647005... | [6.857238292694092, 2.4919168949127197] |
9f80c773-d000-48c8-bd03-8918807605fc | on-the-relevance-of-bandwidth-extension-for | 2202.13865 | null | https://arxiv.org/abs/2202.13865v1 | https://arxiv.org/pdf/2202.13865v1.pdf | On the relevance of bandwidth extension for speaker identification | In this paper we discuss the relevance of bandwidth extension for speaker identification tasks. Mainly we want to study if it is possible to recognize voices that have been bandwith extended. For this purpose, we created two different databases (microphonic and ISDN) of speech signals that were bandwidth extended from ... | ['W. Bastiaan Kleijn', 'Mattias Nilsson', 'Marcos Faundez-Zanuy'] | 2022-02-24 | null | null | null | null | ['bandwidth-extension', 'bandwidth-extension', 'speaker-identification'] | ['audio', 'speech', 'speech'] | [ 6.13078400e-02 -9.39199626e-02 -8.90273526e-02 -3.31623495e-01
-5.70693910e-01 -5.56924164e-01 4.49518025e-01 -4.20054287e-01
-5.80253959e-01 1.01759136e+00 2.89147347e-01 -5.58095098e-01
-3.22978348e-01 -2.28197202e-01 2.33877217e-03 -4.27788109e-01
-3.28707486e-01 2.18308792e-01 3.44943017e-01 -2.03285918... | [14.73827075958252, 5.970371723175049] |
85c45bd7-0287-4ac4-9b84-743d743a59f1 | optimization-of-passive-chip-components | 2001.09612 | null | https://arxiv.org/abs/2001.09612v1 | https://arxiv.org/pdf/2001.09612v1.pdf | Optimization of Passive Chip Components Placement with Self-Alignment Effect for Advanced Surface Mounting Technology | Surface mount technology (SMT) is an enhanced method in electronic packaging in which electronic components are placed directly on soldered printing circuit board (PCB) and are permanently attached on PCB with the aim of reflow soldering process. During reflow process, once deposited solder pastes start melting, electr... | ['Hae-Yong Yang', 'Irandokht Parviziomran', 'Shun Cao', 'Seungbae Park', 'Daehan Won'] | 2020-01-27 | null | null | null | null | ['2d-human-pose-estimation'] | ['computer-vision'] | [ 1.86243296e-01 -1.29342198e-01 -4.25502174e-02 -3.11032623e-01
-8.80796015e-02 -3.34655017e-01 -1.19590508e-02 -2.73025334e-01
1.95466444e-01 6.05629563e-01 -1.97083965e-01 2.04702988e-02
-6.00388944e-01 -5.88350236e-01 -4.73949969e-01 -8.33124638e-01
4.31944877e-01 6.45024598e-01 1.76003456e-01 1.20304301... | [6.492500305175781, 2.75191593170166] |
98708aa0-62a9-46d5-9ac8-b313eeee236d | why-so-pessimistic-estimating-uncertainties | null | null | https://openreview.net/forum?id=wQ7RCayXUSl | https://openreview.net/pdf?id=wQ7RCayXUSl | Why so pessimistic? Estimating uncertainties for offline RL through ensembles, and why their independence matters. | In order to achieve strong performance in offline reinforcement learning (RL), it is necessary to act conservatively with respect to confident lower-bounds on anticipated values of actions. Thus, a valuable approach would be to obtain high quality uncertainty estimates on action values. In current supervised learning ... | ['Ofir Nachum', 'Shixiang Shane Gu', 'Seyed Kamyar Seyed Ghasemipour'] | 2021-09-29 | null | null | null | null | ['d4rl'] | ['robots'] | [-1.53202936e-02 4.12474722e-01 -1.56658083e-01 -1.68232396e-01
-1.07839537e+00 -6.54788673e-01 5.33159256e-01 1.85127124e-01
-5.94942808e-01 1.24872255e+00 -7.84664899e-02 -6.05189025e-01
-4.98628825e-01 -7.16296196e-01 -1.12303925e+00 -7.57302940e-01
-2.51515239e-01 6.40892982e-01 6.03684001e-02 -3.84755552... | [4.156290054321289, 2.4037327766418457] |
4f6dd3be-50cf-41f9-9ebb-9a36eb370227 | scalekd-distilling-scale-aware-knowledge-in | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhu_ScaleKD_Distilling_Scale-Aware_Knowledge_in_Small_Object_Detector_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhu_ScaleKD_Distilling_Scale-Aware_Knowledge_in_Small_Object_Detector_CVPR_2023_paper.pdf | ScaleKD: Distilling Scale-Aware Knowledge in Small Object Detector | Despite the prominent success of general object detection, the performance and efficiency of Small Object Detection (SOD) are still unsatisfactory. Unlike existing works that struggle to balance the trade-off between inference speed and SOD performance, in this paper, we propose a novel Scale-aware Knowledge Distil... | ['Jian Tang', 'Xiaofeng Mou', 'Zhicai Ou', 'Zhiyuan Xu', 'Ning Liu', 'Qiqi Zhou', 'Yichen Zhu'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['small-object-detection'] | ['computer-vision'] | [-2.66669095e-01 9.50973630e-02 4.39722501e-02 -3.99194956e-01
-7.01930583e-01 -5.07866025e-01 4.25746799e-01 1.31280236e-02
-7.04928637e-01 4.24197972e-01 6.56531379e-02 -6.77602366e-02
-1.16883777e-03 -8.65032196e-01 -8.49007249e-01 -6.98543787e-01
4.87288415e-01 2.20847175e-01 9.64359701e-01 -2.17020586... | [9.344827651977539, 1.357892394065857] |
c67313e7-6139-4034-957f-2e4adab9140d | textual-explanations-for-automated-commentary | 2304.08178 | null | https://arxiv.org/abs/2304.08178v1 | https://arxiv.org/pdf/2304.08178v1.pdf | Textual Explanations for Automated Commentary Driving | The provision of natural language explanations for the predictions of deep-learning-based vehicle controllers is critical as it enhances transparency and easy audit. In this work, a state-of-the-art (SOTA) prediction and explanation model is thoroughly evaluated and validated (as a benchmark) on the new Sense--Assess--... | ['Lars Kunze', 'Daniel Omeiza', 'Marc Alexander Kühn'] | 2023-04-12 | null | null | null | null | ['explanation-generation'] | ['natural-language-processing'] | [ 7.23464936e-02 1.03251231e+00 -3.74936521e-01 -5.84018528e-01
-6.68838382e-01 -1.95132449e-01 1.37917137e+00 6.47641122e-02
-1.27371654e-01 8.56956601e-01 3.34371090e-01 -6.92446172e-01
1.01224169e-01 -5.21283150e-01 -9.49836433e-01 -1.49141476e-01
2.65419662e-01 8.14474940e-01 3.48880917e-01 -4.36471522... | [5.920041561126709, 0.9222071766853333] |
a4d957d8-b1fd-44d3-b12f-c8a530f19876 | graghvqa-language-guided-graph-neural | 2104.10283 | null | https://arxiv.org/abs/2104.10283v2 | https://arxiv.org/pdf/2104.10283v2.pdf | GraghVQA: Language-Guided Graph Neural Networks for Graph-based Visual Question Answering | Images are more than a collection of objects or attributes -- they represent a web of relationships among interconnected objects. Scene Graph has emerged as a new modality for a structured graphical representation of images. Scene Graph encodes objects as nodes connected via pairwise relations as edges. To support ques... | ['Zixuan Liu', 'Yanhao Jiang', 'Weixin Liang'] | 2021-04-20 | null | https://aclanthology.org/2021.maiworkshop-1.12 | https://aclanthology.org/2021.maiworkshop-1.12.pdf | naacl-maiworkshop-2021-6 | ['graph-question-answering'] | ['graphs'] | [ 9.88363773e-02 3.94145101e-01 3.48428963e-03 -6.79774284e-01
-4.04119223e-01 -7.36871779e-01 6.84441566e-01 5.35520792e-01
-1.33159354e-01 1.34695709e-01 4.16697651e-01 -7.91419864e-01
-9.92870554e-02 -1.26872134e+00 -8.49198997e-01 -2.00655475e-01
-2.83500761e-01 5.05822301e-01 2.99512804e-01 -1.97421983... | [10.52593994140625, 1.6839485168457031] |
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