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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
6cd6fc82-6a2e-4e62-a8ba-0cae7bc7b77b | dancin-seq2seq-fooling-text-classifiers-with | 1712.05419 | null | http://arxiv.org/abs/1712.05419v1 | http://arxiv.org/pdf/1712.05419v1.pdf | DANCin SEQ2SEQ: Fooling Text Classifiers with Adversarial Text Example Generation | Machine learning models are powerful but fallible. Generating adversarial
examples - inputs deliberately crafted to cause model misclassification or
other errors - can yield important insight into model assumptions and
vulnerabilities. Despite significant recent work on adversarial example
generation targeting image cl... | ['Catherine Wong'] | 2017-12-14 | null | null | null | null | ['adversarial-text'] | ['adversarial'] | [ 1.03511488e+00 6.55958951e-01 6.59513753e-03 -3.35791916e-01
-8.44100416e-01 -9.94616091e-01 9.90056932e-01 -2.25190371e-01
-1.81977645e-01 9.14088488e-01 -4.79725525e-02 -7.08585024e-01
4.03694898e-01 -9.98779476e-01 -9.53606963e-01 -4.70438063e-01
1.25678465e-01 6.13319457e-01 -3.68360370e-01 -3.27416629... | [5.958245277404785, 8.09627628326416] |
050e4f84-6e58-46a1-b7e3-634f659b1af6 | niletmrg-at-semeval-2017-task-8-determining | null | null | https://aclanthology.org/S17-2082 | https://aclanthology.org/S17-2082.pdf | NileTMRG at SemEval-2017 Task 8: Determining Rumour and Veracity Support for Rumours on Twitter. | Final submission for NileTMRG on RumourEval 2017. | ['Samhaa R. El-Beltagy', 'Omar Enayet'] | 2017-08-01 | null | null | null | semeval-2017-8 | ['rumour-detection'] | ['natural-language-processing'] | [-4.36484426e-01 5.17918110e-01 -6.36819720e-01 -2.96494454e-01
-4.69839096e-01 -2.45247424e-01 1.53541207e+00 2.13978499e-01
-3.98187041e-01 1.05964506e+00 9.04842377e-01 -7.72266388e-01
1.92347810e-01 -4.83739614e-01 -1.15649724e+00 -1.63264602e-01
-6.12730384e-01 1.04947305e+00 3.46233606e-01 -9.90166187... | [8.227842330932617, 10.12623405456543] |
6a048045-6f97-43f6-9c19-8467fbffb83e | semi-supervised-word-sense-disambiguation-2 | null | null | https://aclanthology.org/2020.textgraphs-1.6 | https://aclanthology.org/2020.textgraphs-1.6.pdf | Semi-supervised Word Sense Disambiguation Using Example Similarity Graph | Word Sense Disambiguation (WSD) is a well-known problem in the natural language processing. In recent years, there has been increasing interest in applying neural net-works and machine learning techniques to solve WSD problems. However, these previ-ous supervised approaches often suffer from the lack of manually sense-... | ['Minoru Sasaki', 'Rie Yatabe'] | null | null | null | null | coling-textgraphs-2020-12 | ['word-sense-disambiguation'] | ['natural-language-processing'] | [ 7.42091099e-04 -4.92730252e-02 -2.28163943e-01 -2.58869410e-01
-2.84604490e-01 -2.92840421e-01 6.37481868e-01 7.26649225e-01
-6.52911603e-01 9.08784568e-01 2.76916057e-01 -1.75663695e-01
-3.53215009e-01 -1.07070374e+00 -4.29427773e-02 -4.50647414e-01
1.57765105e-01 3.44400704e-01 5.43439329e-01 -4.70724493... | [9.983881950378418, 9.033024787902832] |
dd80ce6b-2ff6-4152-af04-4dab9f942be3 | barcodes-for-medical-image-retrieval-using | 1609.05112 | null | http://arxiv.org/abs/1609.05112v1 | http://arxiv.org/pdf/1609.05112v1.pdf | Barcodes for Medical Image Retrieval Using Autoencoded Radon Transform | Using content-based binary codes to tag digital images has emerged as a
promising retrieval technology. Recently, Radon barcodes (RBCs) have been
introduced as a new binary descriptor for image search. RBCs are generated by
binarization of Radon projections and by assembling them into a vector, namely
the barcode. A si... | ['Hamid. R. Tizhoosh', 'Shamak Dutta', 'Christopher Mitcheltree', 'Shujin Zhu'] | 2016-09-16 | null | null | null | null | ['medical-image-retrieval', 'medical-image-retrieval'] | ['computer-vision', 'medical'] | [ 1.63244799e-01 -3.89651567e-01 8.24660212e-02 -3.82128000e-01
-8.42520475e-01 -1.42827213e-01 5.91233373e-01 4.92398441e-01
-7.24751413e-01 3.93462390e-01 2.59378493e-01 -1.38414726e-01
-3.82603407e-01 -9.72591937e-01 -6.33922875e-01 -1.04446864e+00
-6.96037486e-02 3.58210415e-01 2.00866386e-01 -2.48803329... | [14.24431037902832, -1.4313194751739502] |
d957ff31-76bc-41cd-8807-aaebd654298d | dialogue-enhancement-and-listening-effort-in | 2207.14240 | null | https://arxiv.org/abs/2207.14240v2 | https://arxiv.org/pdf/2207.14240v2.pdf | Dialogue Enhancement and Listening Effort in Broadcast Audio: A Multimodal Evaluation | Dialogue enhancement (DE) plays a vital role in broadcasting, enabling the personalization of the relative level between foreground speech and background music and effects. DE has been shown to improve the quality of experience, intelligibility, and self-reported listening effort (LE). A physiological indicator of LE k... | ['Emanuël A. P. Habets', 'Thomas Robotham', 'Matteo Torcoli'] | 2022-07-28 | null | null | null | null | ['pupil-dilation'] | ['computer-vision'] | [ 1.85853332e-01 -4.21306072e-03 2.83347577e-01 8.81906673e-02
-6.69233680e-01 -6.81314588e-01 7.97848180e-02 5.52511692e-01
-7.84727395e-01 7.18442678e-01 5.08594811e-01 1.03885256e-01
-1.33256719e-01 -4.47295398e-01 -2.97324687e-01 -7.69151509e-01
1.07461862e-01 -3.65308583e-01 1.50108770e-01 -2.68638968... | [15.105430603027344, 5.704535007476807] |
8ab2c548-4479-4ab2-812a-be0107b9f68b | full-resolution-quality-assessment-for | 2108.06144 | null | https://arxiv.org/abs/2108.06144v3 | https://arxiv.org/pdf/2108.06144v3.pdf | Full-resolution quality assessment for pansharpening | A reliable quality assessment procedure for pansharpening methods is of critical importance for the development of the related solutions. Unfortunately, the lack of ground-truths to be used as guidance for an objective evaluation has pushed the community to resort to two approaches which can also be jointly applied. He... | ['Matteo Ciotola', 'Giuseppe Scarpa'] | 2021-08-13 | null | null | null | null | ['pansharpening'] | ['computer-vision'] | [ 5.75819850e-01 -3.41317862e-01 -6.17792867e-02 -2.30016112e-01
-1.11097419e+00 -4.40295070e-01 5.55988789e-01 8.90657082e-02
-2.14399472e-01 8.73008430e-01 -1.02616765e-01 1.51416570e-01
-8.33724916e-01 -1.05710506e+00 -2.02396326e-02 -1.07787955e+00
3.04002881e-01 -1.32281650e-02 1.47172898e-01 -5.01786411... | [10.064818382263184, -2.0790069103240967] |
50ab34d9-9a26-4677-a5f4-cd9cc15aeeb7 | dict2vec-learning-word-embeddings-using | null | null | https://aclanthology.org/D17-1024 | https://aclanthology.org/D17-1024.pdf | Dict2vec : Learning Word Embeddings using Lexical Dictionaries | Learning word embeddings on large unlabeled corpus has been shown to be successful in improving many natural language tasks. The most efficient and popular approaches learn or retrofit such representations using additional external data. Resulting embeddings are generally better than their corpus-only counterparts, alt... | ['Julien Tissier', 'Christophe Gravier', 'Amaury Habrard'] | 2017-09-01 | null | null | null | emnlp-2017-9 | ['learning-word-embeddings'] | ['methodology'] | [-2.49518022e-01 -1.18381597e-01 -5.78130960e-01 -4.61297274e-01
-4.71443325e-01 -6.75744355e-01 7.88772106e-01 8.69265139e-01
-1.06890202e+00 5.12437522e-01 8.56506348e-01 -1.72196731e-01
1.40577465e-01 -8.15884233e-01 -1.29849166e-01 -4.32721049e-01
2.48182595e-01 8.25618088e-01 -8.91922563e-02 -7.85368502... | [10.454265594482422, 8.758281707763672] |
5ed219c7-b04d-4b11-9210-390062ac4182 | a-review-of-deep-learning-powered-mesh | 2303.02879 | null | https://arxiv.org/abs/2303.02879v1 | https://arxiv.org/pdf/2303.02879v1.pdf | A Review of Deep Learning-Powered Mesh Reconstruction Methods | With the recent advances in hardware and rendering techniques, 3D models have emerged everywhere in our life. Yet creating 3D shapes is arduous and requires significant professional knowledge. Meanwhile, Deep learning has enabled high-quality 3D shape reconstruction from various sources, making it a viable approach to ... | ['Zhiqin Chen'] | 2023-03-06 | null | null | null | null | ['3d-shape-reconstruction'] | ['computer-vision'] | [-6.59932718e-02 -8.77242833e-02 2.55012065e-01 -2.21778616e-01
-4.76001710e-01 -4.81165051e-01 3.62932026e-01 -9.30847377e-02
2.94809073e-01 3.33874851e-01 -2.44533435e-01 -2.17804909e-01
7.32103586e-02 -1.17846882e+00 -8.96033287e-01 -4.06507909e-01
-1.16456054e-01 8.17540705e-01 4.69440371e-02 -1.82816952... | [8.649104118347168, -3.6265709400177] |
d85305a1-8d1e-4836-ba96-d94f6658e1df | the-blessing-of-heterogeneity-in-federated-q | 2305.10697 | null | https://arxiv.org/abs/2305.10697v1 | https://arxiv.org/pdf/2305.10697v1.pdf | The Blessing of Heterogeneity in Federated Q-learning: Linear Speedup and Beyond | When the data used for reinforcement learning (RL) are collected by multiple agents in a distributed manner, federated versions of RL algorithms allow collaborative learning without the need of sharing local data. In this paper, we consider federated Q-learning, which aims to learn an optimal Q-function by periodically... | ['Yuejie Chi', 'Gauri Joshi', 'Jiin Woo'] | 2023-05-18 | null | null | null | null | ['q-learning'] | ['methodology'] | [-4.60474968e-01 8.23655427e-02 -4.23676997e-01 1.72415644e-01
-1.21264088e+00 -7.98156202e-01 5.47858715e-01 5.47609687e-01
-7.79986560e-01 1.09111166e+00 1.82857394e-01 -2.11598694e-01
-5.09513259e-01 -8.17461193e-01 -5.84386587e-01 -1.03804052e+00
-5.91895819e-01 6.77598298e-01 1.39584750e-01 -7.50787929... | [4.079892635345459, 2.3608789443969727] |
1a4d3b2d-e309-48de-a2ee-14ab6398412e | guided-image-synthesis-via-initial-image | 2305.03382 | null | https://arxiv.org/abs/2305.03382v1 | https://arxiv.org/pdf/2305.03382v1.pdf | Guided Image Synthesis via Initial Image Editing in Diffusion Model | Diffusion models have the ability to generate high quality images by denoising pure Gaussian noise images. While previous research has primarily focused on improving the control of image generation through adjusting the denoising process, we propose a novel direction of manipulating the initial noise to control the gen... | ['Kiyoharu Aizawa', 'Xueting Wang', 'Jiafeng Mao'] | 2023-05-05 | null | null | null | null | ['layout-to-image-generation', 'image-manipulation'] | ['computer-vision', 'computer-vision'] | [ 7.16566563e-01 5.06427176e-02 1.18080959e-01 -2.28401557e-01
-4.40561652e-01 -7.47900367e-01 5.63786566e-01 -2.15008065e-01
-2.83658475e-01 4.72754955e-01 4.10046041e-01 -9.60562751e-02
7.92744681e-02 -9.34826493e-01 -7.20054150e-01 -9.41424251e-01
1.53618917e-01 -3.93661186e-02 3.61779302e-01 -3.64916980... | [11.411520957946777, -0.371844083070755] |
35ecbb6c-44aa-4a46-97c7-cc9a4eadc54f | visual-aware-attention-dual-stream-decoder | 2110.08578 | null | https://arxiv.org/abs/2110.08578v1 | https://arxiv.org/pdf/2110.08578v1.pdf | Visual-aware Attention Dual-stream Decoder for Video Captioning | Video captioning is a challenging task that captures different visual parts and describes them in sentences, for it requires visual and linguistic coherence. The attention mechanism in the current video captioning method learns to assign weight to each frame, promoting the decoder dynamically. This may not explicitly m... | ['Luo Zhong', 'Lin Li', 'Shuqin Chen', 'Xian Zhong', 'Zhixin Sun'] | 2021-10-16 | null | null | null | null | ['video-description'] | ['computer-vision'] | [ 3.79059255e-01 -1.91469014e-01 -1.53827071e-01 -1.91259071e-01
-4.32208121e-01 -1.91274017e-01 7.14429557e-01 -3.23977590e-01
-3.36076885e-01 7.72698700e-01 6.62091076e-01 2.81386506e-02
4.13016826e-01 -4.56393719e-01 -9.81978953e-01 -7.29200363e-01
3.45975280e-01 -5.13677020e-03 3.26120853e-01 -1.62310019... | [10.532060623168945, 0.6697837710380554] |
fd61b8e0-f6af-4693-b34c-faf87245418d | hybrid-feature-learning-for-handwriting | 1812.02621 | null | https://arxiv.org/abs/1812.02621v1 | https://arxiv.org/pdf/1812.02621v1.pdf | Hybrid Feature Learning for Handwriting Verification | We propose an effective Hybrid Deep Learning (HDL) architecture for the task of determining the probability that a questioned handwritten word has been written by a known writer. HDL is an amalgamation of Auto-Learned Features (ALF) and Human-Engineered Features (HEF). To extract auto-learned features we use two method... | ['Sargur Srihari', 'Jun Chu', 'Mihir Chauhan', 'Mohammad Abuzar Shaikh'] | 2018-11-19 | null | null | null | null | ['handwriting-verification'] | ['computer-vision'] | [-1.04172520e-01 -2.12035328e-01 4.24148887e-01 -4.51056182e-01
-6.54932439e-01 -8.47661316e-01 8.73727083e-01 -3.87061894e-01
-4.01538759e-01 6.51950061e-01 1.50215983e-01 -1.59257442e-01
-5.13710193e-02 -8.06661427e-01 -7.60992885e-01 -6.43699169e-01
4.08470333e-01 4.17623699e-01 6.19328246e-02 -3.72567117... | [11.836752891540527, 2.689094066619873] |
1992c67a-c270-43aa-af46-d5d21965c62d | pretraining-on-interactions-for-learning | 2207.02272 | null | https://arxiv.org/abs/2207.02272v1 | https://arxiv.org/pdf/2207.02272v1.pdf | Pretraining on Interactions for Learning Grounded Affordance Representations | Lexical semantics and cognitive science point to affordances (i.e. the actions that objects support) as critical for understanding and representing nouns and verbs. However, study of these semantic features has not yet been integrated with the "foundation" models that currently dominate language representation research... | ['Ellie Pavlick', 'Carsten Eickhoff', 'Dylan Ebert', 'Jack Merullo'] | 2022-07-05 | null | https://aclanthology.org/2022.starsem-1.23 | https://aclanthology.org/2022.starsem-1.23.pdf | sem-naacl-2022-7 | ['grounded-language-learning'] | ['natural-language-processing'] | [ 1.06195234e-01 2.80733734e-01 -3.90917271e-01 -4.81662840e-01
1.52519895e-02 -6.27328634e-01 1.18260145e+00 5.09901762e-01
-2.12217197e-01 2.80097842e-01 7.88422585e-01 -3.61914784e-01
-1.89995930e-01 -1.02620411e+00 -6.68462694e-01 -3.72279644e-01
-3.68740559e-01 5.10008276e-01 8.43102336e-02 -3.88804674... | [9.491263389587402, 6.977221965789795] |
a7b74cd3-e2ff-41d2-b989-f80235f45300 | umse-unified-multi-scenario-summarization | 2305.16895 | null | https://arxiv.org/abs/2305.16895v1 | https://arxiv.org/pdf/2305.16895v1.pdf | UMSE: Unified Multi-scenario Summarization Evaluation | Summarization quality evaluation is a non-trivial task in text summarization. Contemporary methods can be mainly categorized into two scenarios: (1) reference-based: evaluating with human-labeled reference summary; (2) reference-free: evaluating the summary consistency of the document. Recent studies mainly focus on on... | ['Zhumin Chen', 'Zhaochun Ren', 'Pengjie Ren', 'Xiuying Chen', 'Chongyang Tao', 'Zhitao Yao', 'Shen Gao'] | 2023-05-26 | null | null | null | null | ['text-summarization'] | ['natural-language-processing'] | [ 3.80868554e-01 4.95366715e-02 -2.23146290e-01 -2.67342806e-01
-1.05227613e+00 -5.03769279e-01 6.18029416e-01 3.28533322e-01
-3.88349712e-01 8.32036376e-01 6.75008714e-01 -7.22529367e-02
-1.50236785e-01 -5.10380328e-01 -5.15838861e-01 -3.82263303e-01
5.13587773e-01 3.76384288e-01 2.87418783e-01 -2.67497927... | [12.245711326599121, 9.288104057312012] |
f2548a8d-9ac1-4862-a594-6f6251f67321 | how-to-choose-how-to-choose-your-chatbot-a | 2305.14533 | null | https://arxiv.org/abs/2305.14533v1 | https://arxiv.org/pdf/2305.14533v1.pdf | How to Choose How to Choose Your Chatbot: A Massively Multi-System MultiReference Data Set for Dialog Metric Evaluation | We release MMSMR, a Massively Multi-System MultiReference dataset to enable future work on metrics and evaluation for dialog. Automatic metrics for dialogue evaluation should be robust proxies for human judgments; however, the verification of robustness is currently far from satisfactory. To quantify the robustness cor... | ['João Sedoc', 'Edward Cohen', 'Zuhaib Akhtar', 'Huda Khayrallah'] | 2023-05-23 | null | null | null | null | ['chatbot', 'dialogue-evaluation', 'chatbot'] | ['methodology', 'natural-language-processing', 'natural-language-processing'] | [-3.70104730e-01 2.06782088e-01 1.11709900e-01 -8.40900600e-01
-1.10176611e+00 -1.11982751e+00 1.10750628e+00 -2.09959727e-02
-6.19549274e-01 1.02906752e+00 8.80369902e-01 -3.16016495e-01
1.93194603e-04 -2.54169166e-01 9.73982140e-02 -1.14680625e-01
7.30402842e-02 1.02759004e+00 3.71713042e-01 -6.85687721... | [12.833789825439453, 8.007457733154297] |
61f51547-9386-4814-8a88-d5fecd899037 | pre-trained-language-model-with-prompts-for | 2305.07912 | null | https://arxiv.org/abs/2305.07912v1 | https://arxiv.org/pdf/2305.07912v1.pdf | Pre-trained Language Model with Prompts for Temporal Knowledge Graph Completion | Temporal Knowledge graph completion (TKGC) is a crucial task that involves reasoning at known timestamps to complete the missing part of facts and has attracted more and more attention in recent years. Most existing methods focus on learning representations based on graph neural networks while inaccurately extracting i... | ['Min Peng', 'Xu Jia', 'Miao Peng', 'Ben Liu', 'Wenjie Xu'] | 2023-05-13 | null | null | null | null | ['knowledge-graph-completion', 'temporal-knowledge-graph-completion'] | ['knowledge-base', 'knowledge-base'] | [ 5.57652786e-02 4.23668236e-01 -6.39381170e-01 -5.82753897e-01
-6.43360436e-01 -3.65452766e-01 6.65419281e-01 5.36139190e-01
-3.21870923e-01 7.73088932e-01 5.37004054e-01 -4.66401160e-01
1.85841486e-01 -1.17569363e+00 -9.15227413e-01 -4.17949781e-02
-2.90442824e-01 2.44070277e-01 4.47978735e-01 -2.56826162... | [8.596556663513184, 7.952519416809082] |
a78cba5f-aa7b-47c1-afde-dd17555a58d7 | model-debiasing-via-gradient-based | 2305.12178 | null | https://arxiv.org/abs/2305.12178v1 | https://arxiv.org/pdf/2305.12178v1.pdf | Model Debiasing via Gradient-based Explanation on Representation | Machine learning systems produce biased results towards certain demographic groups, known as the fairness problem. Recent approaches to tackle this problem learn a latent code (i.e., representation) through disentangled representation learning and then discard the latent code dimensions correlated with sensitive attrib... | ['Lei Chen', 'Caleb Chen Cao', 'Yongxiang Huang', 'Dan Su', 'Luning Wang', 'Jindi Zhang'] | 2023-05-20 | null | null | null | null | ['disentanglement'] | ['methodology'] | [ 3.16476971e-02 3.07673603e-01 -7.66104937e-01 -6.16566420e-01
-4.95988727e-01 -3.80030245e-01 5.21668911e-01 1.91033423e-01
-3.23902041e-01 9.83694553e-01 7.64692068e-01 -2.12590352e-01
-2.54454941e-01 -8.23299944e-01 -2.03652796e-03 -7.40324914e-01
2.05422983e-01 6.45919383e-01 -6.47263646e-01 -5.73044494... | [8.97080135345459, 5.279632568359375] |
555e4823-b6cc-4cab-a999-c87d9943c108 | contrastive-language-image-pre-training-for | 2108.08688 | null | https://arxiv.org/abs/2108.08688v1 | https://arxiv.org/pdf/2108.08688v1.pdf | Contrastive Language-Image Pre-training for the Italian Language | CLIP (Contrastive Language-Image Pre-training) is a very recent multi-modal model that jointly learns representations of images and texts. The model is trained on a massive amount of English data and shows impressive performance on zero-shot classification tasks. Training the same model on a different language is not t... | ['Sri Lakshmi', 'Gabriele Sarti', 'Silvia Terragni', 'Raphael Pisoni', 'Giuseppe Attanasio', 'Federico Bianchi'] | 2021-08-19 | null | null | null | null | ['multi-label-zero-shot-learning'] | ['computer-vision'] | [-1.25429686e-02 -3.70677292e-01 -2.40843967e-01 -2.83557922e-01
-1.41950905e+00 -3.44290406e-01 9.44126904e-01 5.12915775e-02
-7.91262746e-01 4.05779213e-01 2.98378885e-01 7.12913089e-03
5.12677968e-01 -3.87652278e-01 -8.20166528e-01 -4.95064348e-01
3.08647454e-01 8.35207224e-01 3.24225038e-01 -2.54382432... | [11.113675117492676, 1.6148475408554077] |
96136361-1a47-49a3-9cfe-7cdb1a55f56f | automated-generation-of-accurate-fluent-1 | null | null | https://aclanthology.org/2021.emnlp-main.288 | https://aclanthology.org/2021.emnlp-main.288.pdf | Automated Generation of Accurate & Fluent Medical X-ray Reports | Our paper aims to automate the generation of medical reports from chest X-ray image inputs, a critical yet time-consuming task for radiologists. Existing medical report generation efforts emphasize producing human-readable reports, yet the generated text may not be well aligned to the clinical facts. Our generated medi... | ['Li Cheng', 'Jason Truong', 'Yingying Zhu', 'Yujie Liu', 'Taivanbat Badamdorj', 'Dong Nie', 'Hoang Nguyen'] | null | null | null | null | emnlp-2021-11 | ['medical-report-generation'] | ['medical'] | [ 3.19939375e-01 6.28013074e-01 -1.01505876e-01 -4.24099058e-01
-1.36812282e+00 -4.30784881e-01 5.43673337e-01 3.66080344e-01
-1.99548930e-01 9.11295831e-01 7.68963456e-01 -4.42925662e-01
-1.94987744e-01 -7.91141808e-01 -2.14890912e-01 -3.91170949e-01
-2.32162476e-02 7.21688271e-01 -1.88333124e-01 4.56150249... | [15.038351058959961, -1.387242317199707] |
873cda03-e1f8-408f-b7b6-3c556bfbb40b | alien-coding | 2301.11479 | null | https://arxiv.org/abs/2301.11479v1 | https://arxiv.org/pdf/2301.11479v1.pdf | Alien Coding | We introduce a self-learning algorithm for synthesizing programs for OEIS sequences. The algorithm starts from scratch initially generating programs at random. Then it runs many iterations of a self-learning loop that interleaves (i) training neural machine translation to learn the correspondence between sequences and ... | ['Josef Urban', 'Miroslav Olšák', 'Thibault Gauthier'] | 2023-01-27 | null | null | null | null | ['self-learning'] | ['natural-language-processing'] | [ 4.68381107e-01 4.80903625e-01 -7.37615347e-01 -4.43238825e-01
-5.43398738e-01 -8.21950495e-01 4.08191621e-01 -1.44592091e-01
-1.70303807e-01 7.47757494e-01 -1.43946454e-01 -7.63999581e-01
4.39425349e-01 -9.29406345e-01 -1.46966124e+00 -2.63758183e-01
-2.47626811e-01 7.56870806e-01 3.30647558e-01 -3.73586863... | [8.184425354003906, 7.442819595336914] |
d3fe0631-b2b9-40c5-bf2e-44a312160c4d | probabilistic-human-like-gesture-synthesis | null | null | https://openreview.net/forum?id=ykvm7OLh7B | https://openreview.net/pdf?id=ykvm7OLh7B | Probabilistic Human-like Gesture Synthesis from Speech using GRU-based WGAN | Gestures are crucial for increasing the human-likeness of agents and robots to achieve smoother interactions with humans. The realization of an effective system to model human gestures, which are matched with the speech utterances, is necessary to be embedded in these agents. In this work, we propose a GRU-based autore... | ['Hiroshi Ishiguro', 'Carlos Ishi', 'Chaoran Liu', 'Bowen Wu'] | 2021-07-05 | null | null | null | acm-icmi-workshop-genea-2021-10 | ['gesture-generation'] | ['robots'] | [ 1.43715948e-01 4.44423705e-01 5.87302446e-02 -1.41493110e-02
-4.19483215e-01 -4.63167995e-01 9.70286548e-01 -1.06928945e+00
-2.99280733e-01 5.64697564e-01 3.20316583e-01 7.61037394e-02
5.16333997e-01 -7.87129462e-01 -7.18976915e-01 -9.96449888e-01
1.83896258e-01 5.52257121e-01 1.64300506e-03 -4.33864117... | [5.710224151611328, -0.09714796394109726] |
1355d28e-c88d-4721-9f53-6ffa3a012be9 | seqdiffuseq-text-diffusion-with-encoder | 2212.10325 | null | https://arxiv.org/abs/2212.10325v5 | https://arxiv.org/pdf/2212.10325v5.pdf | SeqDiffuSeq: Text Diffusion with Encoder-Decoder Transformers | Diffusion model, a new generative modelling paradigm, has achieved great success in image, audio, and video generation. However, considering the discrete categorical nature of text, it is not trivial to extend continuous diffusion models to natural language, and text diffusion models are less studied. Sequence-to-seque... | ['Songfang Huang', 'Fei Huang', 'Chuanqi Tan', 'Zheng Yuan', 'Hongyi Yuan'] | 2022-12-20 | null | null | null | null | ['video-generation'] | ['computer-vision'] | [ 4.24688518e-01 -1.40714362e-01 1.76164165e-01 -1.28625736e-01
-5.78814805e-01 -3.68843883e-01 9.23675656e-01 -2.22854689e-01
-1.69483840e-01 7.97789097e-01 6.57203436e-01 -2.55362123e-01
2.87565365e-02 -1.15782964e+00 -3.52788687e-01 -7.90138900e-01
1.88951626e-01 3.87773603e-01 2.57521123e-01 -4.82373774... | [11.9642915725708, 9.040743827819824] |
0904c258-13c9-478d-8bb1-9a17141c6e1f | deep-declarative-dynamic-time-warping-for-end | 2303.10778 | null | https://arxiv.org/abs/2303.10778v1 | https://arxiv.org/pdf/2303.10778v1.pdf | Deep Declarative Dynamic Time Warping for End-to-End Learning of Alignment Paths | This paper addresses learning end-to-end models for time series data that include a temporal alignment step via dynamic time warping (DTW). Existing approaches to differentiable DTW either differentiate through a fixed warping path or apply a differentiable relaxation to the min operator found in the recursive steps us... | ['Stephen Gould', 'Michael Milford', 'Sourav Garg', 'Ming Xu'] | 2023-03-19 | null | null | null | null | ['visual-place-recognition', 'music-information-retrieval', 'dynamic-time-warping'] | ['computer-vision', 'music', 'time-series'] | [ 4.14032996e-01 1.33651227e-01 -3.14710755e-03 -4.09633189e-01
-1.05344129e+00 -7.72854805e-01 5.01511693e-01 8.66255388e-02
-6.49693191e-01 2.89749712e-01 2.11136431e-01 -6.69281855e-02
-6.59408450e-01 -3.56480241e-01 -9.24714506e-01 -7.32056558e-01
-5.78761697e-01 6.34448290e-01 -2.04392388e-01 -4.14928883... | [7.396464824676514, 3.2908692359924316] |
6e06a80a-62d2-4ddc-944f-f59d1f8cc1f5 | machine-learning-friendly-biomedical-datasets | 2205.03447 | null | https://arxiv.org/abs/2205.03447v7 | https://arxiv.org/pdf/2205.03447v7.pdf | Machine Learning-Friendly Biomedical Datasets for Equivalence and Subsumption Ontology Matching | Ontology Matching (OM) plays an important role in many domains such as bioinformatics and the Semantic Web, and its research is becoming increasingly popular, especially with the application of machine learning (ML) techniques. Although the Ontology Alignment Evaluation Initiative (OAEI) represents an impressive effort... | ['Ian Horrocks', 'Ali Hadian', 'Ernesto Jiménez-Ruiz', 'Hang Dong', 'Jiaoyan Chen', 'Yuan He'] | 2022-05-06 | null | null | null | null | ['ontology-matching'] | ['knowledge-base'] | [ 3.46998960e-01 3.38545382e-01 -4.08334047e-01 -2.26625204e-01
-4.52870727e-01 -1.98925361e-01 4.24316615e-01 8.09111774e-01
-4.12289441e-01 7.94824958e-01 8.83413553e-02 -3.33973616e-01
-8.30822587e-01 -8.53895426e-01 -3.05081248e-01 -5.49331494e-02
-1.63391218e-01 8.19447517e-01 3.60498697e-01 -3.11958909... | [9.143705368041992, 8.117443084716797] |
f2129ffc-3dd3-46d4-af70-a8fa6aec0b77 | baco-a-fast-and-portable-bayesian-compiler | 2212.11142 | null | https://arxiv.org/abs/2212.11142v2 | https://arxiv.org/pdf/2212.11142v2.pdf | BaCO: A Fast and Portable Bayesian Compiler Optimization Framework | We introduce the Bayesian Compiler Optimization framework (BaCO), a general purpose autotuner for modern compilers targeting CPUs, GPUs, and FPGAs. BaCO provides the flexibility needed to handle the requirements of modern autotuning tasks. Particularly, it deals with permutation, ordered, and continuous parameter types... | ['Luigi Nardi', 'Kunle Olukotun', 'Michel Steuwer', 'Fredrik Kjolstad', 'Adel Ejjeh', 'Olivia Hsu', 'Rubens Lacouture', 'Johannes Lenfers', 'Artur Souza', 'Erik Hellsten'] | 2022-12-01 | null | null | null | null | ['compiler-optimization'] | ['computer-code'] | [-3.73442441e-01 -4.89966512e-01 -5.05068779e-01 -1.69375181e-01
-6.05300546e-01 -7.88518727e-01 5.64408839e-01 9.85828489e-02
-1.29690737e-01 6.58007026e-01 -6.51155487e-02 -1.05845082e+00
-4.12134044e-02 -7.01183677e-01 -6.55774891e-01 -5.71872413e-01
-1.16699912e-01 6.02202177e-01 2.74197668e-01 -2.82645762... | [6.076806545257568, 3.6123530864715576] |
9da764f2-b737-45ae-99f7-579eb2106494 | elastic-decision-transformer | 2307.02484 | null | https://arxiv.org/abs/2307.02484v2 | https://arxiv.org/pdf/2307.02484v2.pdf | Elastic Decision Transformer | This paper introduces Elastic Decision Transformer (EDT), a significant advancement over the existing Decision Transformer (DT) and its variants. Although DT purports to generate an optimal trajectory, empirical evidence suggests it struggles with trajectory stitching, a process involving the generation of an optimal o... | ['Masashi Hamaya', 'Xiaolong Wang', 'Yueh-Hua Wu'] | 2023-07-05 | null | null | null | null | ['q-learning', 'atari-games', 'd4rl'] | ['methodology', 'playing-games', 'robots'] | [-3.23819280e-01 -7.55458251e-02 -6.33034766e-01 6.39216974e-02
-9.79855716e-01 -7.00874865e-01 3.93274426e-01 4.68635485e-02
-3.50608826e-01 9.40462530e-01 3.46996069e-01 -4.31113720e-01
-4.87330616e-01 -7.39524245e-01 -7.36133397e-01 -8.68527651e-01
-2.95652688e-01 7.99820781e-01 3.90195608e-01 -3.70045424... | [4.063046932220459, 2.149888753890991] |
a99c487f-4c42-4e33-a075-509db5447747 | multi-task-generative-adversarial-network-for | 1811.10419 | null | http://arxiv.org/abs/1811.10419v1 | http://arxiv.org/pdf/1811.10419v1.pdf | Multi-Task Generative Adversarial Network for Handling Imbalanced Clinical Data | We propose a new generative adversarial architecture to mitigate imbalance
data problem for the task of medical image semantic segmentation where the
majority of pixels belong to a healthy region and few belong to lesion or
non-health region. A model trained with imbalanced data tends to bias towards
healthy data which... | ['Mina Rezaei', 'Haojin Yang', 'Christoph Meinel'] | 2018-11-22 | null | null | null | null | ['cardiac-segmentation'] | ['medical'] | [ 6.05519176e-01 6.82343304e-01 -1.50922760e-01 -5.15318394e-01
-9.80857968e-01 -2.53755033e-01 2.63818562e-01 -3.07128221e-01
-2.79845238e-01 8.00788105e-01 -1.66344512e-02 -2.17740193e-01
6.46876097e-01 -8.93630028e-01 -7.94785321e-01 -9.40602541e-01
2.18838528e-01 9.71577704e-01 2.38593131e-01 7.80620649... | [14.350927352905273, -2.077425956726074] |
3d6a5bbb-b953-4b1c-bcd3-da6366c6e2a0 | unsupervised-cd-in-satellite-image-time | 2304.11375 | null | https://arxiv.org/abs/2304.11375v1 | https://arxiv.org/pdf/2304.11375v1.pdf | Unsupervised CD in satellite image time series by contrastive learning and feature tracking | While unsupervised change detection using contrastive learning has been significantly improved the performance of literature techniques, at present, it only focuses on the bi-temporal change detection scenario. Previous state-of-the-art models for image time-series change detection often use features obtained by learni... | ['Lorenzo Bruzzone', 'Yuxing Chen'] | 2023-04-22 | null | null | null | null | ['change-detection'] | ['computer-vision'] | [ 5.63397229e-01 -7.40958571e-01 1.08335093e-01 -6.38195336e-01
-7.65587628e-01 -6.91713154e-01 8.69026899e-01 6.73371404e-02
-4.05520439e-01 5.34790397e-01 -1.03507258e-01 -1.66133255e-01
-4.46756691e-01 -9.46338117e-01 -6.90467536e-01 -1.02120459e+00
-5.95753491e-01 -1.23872571e-02 2.65167266e-01 -1.68694824... | [9.66469955444336, -1.3461133241653442] |
423bce38-6bd5-4ff7-b35d-8e2b47a17561 | valuenet-a-new-dataset-for-human-value-driven | 2112.06346 | null | https://arxiv.org/abs/2112.06346v1 | https://arxiv.org/pdf/2112.06346v1.pdf | ValueNet: A New Dataset for Human Value Driven Dialogue System | Building a socially intelligent agent involves many challenges, one of which is to teach the agent to speak guided by its value like a human. However, value-driven chatbots are still understudied in the area of dialogue systems. Most existing datasets focus on commonsense reasoning or social norm modeling. In this work... | ['Song-Chun Zhu', 'Jianfeng Gao', 'Baolin Peng', 'Pan Lu', 'Jinchao Li', 'Yizhou Zhao', 'Liang Qiu'] | 2021-12-12 | null | null | null | null | ['empathetic-response-generation'] | ['natural-language-processing'] | [-3.70041192e-01 6.41011357e-01 -4.12327617e-01 -6.74259007e-01
-2.18909800e-01 -2.42870584e-01 7.81401575e-01 -2.56275564e-01
-2.07555637e-01 1.05859959e+00 8.90889406e-01 2.67219812e-01
3.66737805e-02 -7.57192910e-01 5.33901379e-02 -6.06717825e-01
3.14516008e-01 8.37653518e-01 -4.70756054e-01 -1.23398590... | [13.104767799377441, 7.698455333709717] |
822d83ff-e928-43e9-90e6-d8fa96cc9175 | beats-audio-pre-training-with-acoustic | 2212.09058 | null | https://arxiv.org/abs/2212.09058v1 | https://arxiv.org/pdf/2212.09058v1.pdf | BEATs: Audio Pre-Training with Acoustic Tokenizers | The massive growth of self-supervised learning (SSL) has been witnessed in language, vision, speech, and audio domains over the past few years. While discrete label prediction is widely adopted for other modalities, the state-of-the-art audio SSL models still employ reconstruction loss for pre-training. Compared with r... | ['Furu Wei', 'Zhuo Chen', 'Daniel Tompkins', 'Shujie Liu', 'Chengyi Wang', 'Yu Wu', 'Sanyuan Chen'] | 2022-12-18 | null | null | null | null | ['audio-classification'] | ['audio'] | [ 4.38615233e-01 1.97503284e-01 -1.70514286e-01 -4.64409977e-01
-1.31687677e+00 -3.11491132e-01 2.95950174e-01 -4.95805033e-02
-1.60749152e-01 3.99345845e-01 3.74456972e-01 2.88708396e-02
2.35972449e-01 -4.61795539e-01 -8.55779111e-01 -7.34550655e-01
-8.43781307e-02 3.56952876e-01 1.34951845e-01 1.55355990... | [15.287347793579102, 5.1697611808776855] |
d3e477a9-b4a3-4147-b622-a4b350cd9f6b | quantum-natural-language-generation-on-near | 2211.00727 | null | https://arxiv.org/abs/2211.00727v1 | https://arxiv.org/pdf/2211.00727v1.pdf | Quantum Natural Language Generation on Near-Term Devices | The emergence of noisy medium-scale quantum devices has led to proof-of-concept applications for quantum computing in various domains. Examples include Natural Language Processing (NLP) where sentence classification experiments have been carried out, as well as procedural generation, where tasks such as geopolitical ma... | ['James Wootton', 'Marcel Pfaffhauser', 'Amin Karamlou'] | 2022-11-01 | null | null | null | null | ['music-generation', 'image-manipulation', 'music-generation', 'sentence-classification'] | ['audio', 'computer-vision', 'music', 'natural-language-processing'] | [ 7.61039972e-01 1.70941189e-01 5.31880975e-01 -2.00132921e-01
-9.18783009e-01 -6.60747528e-01 8.92556965e-01 3.24395508e-01
-4.89467889e-01 8.88675034e-01 -5.94612062e-02 -5.29342473e-01
-5.94571866e-02 -1.40767515e+00 -4.82469350e-01 -7.90778399e-01
-7.32491836e-02 3.91041845e-01 -3.32094133e-02 -7.62864530... | [5.595205307006836, 4.940205097198486] |
c9458c6c-a03d-454b-8591-5eba671f8b9a | irnet-instance-relation-network-for | 1908.06623 | null | https://arxiv.org/abs/1908.06623v1 | https://arxiv.org/pdf/1908.06623v1.pdf | IRNet: Instance Relation Network for Overlapping Cervical Cell Segmentation | Cell instance segmentation in Pap smear image remains challenging due to the wide existence of occlusion among translucent cytoplasm in cell clumps. Conventional methods heavily rely on accurate nuclei detection results and are easily disturbed by miscellaneous objects. In this paper, we propose a novel Instance Relati... | ['Pheng-Ann Heng', 'Qi Dou', 'Yanning Zhou', 'Hao Chen', 'Jiaqi Xu'] | 2019-08-19 | null | null | null | null | ['miscellaneous'] | ['miscellaneous'] | [ 4.02864069e-01 1.69024482e-01 -3.87747973e-01 -2.28972033e-01
-7.68586278e-01 -6.54564619e-01 2.62038887e-01 3.89619738e-01
-1.29042894e-01 9.90042329e-01 2.82591383e-04 -1.20943993e-01
-2.72121310e-01 -7.90370584e-01 -3.63001823e-01 -1.04422903e+00
1.98955789e-01 5.89107811e-01 3.36792201e-01 3.07131022... | [15.025507926940918, -3.0195059776306152] |
e6e25552-9288-493d-8943-829d22504b00 | 190600781 | 1906.00781 | null | https://arxiv.org/abs/1906.00781v1 | https://arxiv.org/pdf/1906.00781v1.pdf | Learning Semantic Annotations for Tabular Data | The usefulness of tabular data such as web tables critically depends on understanding their semantics. This study focuses on column type prediction for tables without any meta data. Unlike traditional lexical matching-based methods, we propose a deep prediction model that can fully exploit a table's contextual semantic... | ['Ernesto Jimenez-Ruiz', 'Jiaoyan Chen', 'Charles Sutton', 'Ian Horrocks'] | 2019-05-30 | null | null | null | null | ['type-prediction', 'table-annotation', 'table-annotation', 'column-type-annotation'] | ['computer-code', 'knowledge-base', 'natural-language-processing', 'natural-language-processing'] | [-2.59198070e-01 2.16264516e-01 -8.64710689e-01 -7.29635775e-01
-9.13449109e-01 -6.49271429e-01 1.97609842e-01 1.12627459e+00
-6.06182478e-02 9.82534528e-01 4.19473737e-01 -5.11423767e-01
-9.88068152e-03 -1.66113567e+00 -1.04822040e+00 9.81578678e-02
8.25092755e-03 9.10071135e-01 4.99437898e-01 -6.88922584... | [9.646164894104004, 7.844903945922852] |
7ba21dfa-e37e-488f-9165-65d0624efa9b | deep-learning-based-face-super-resolution-a | 2101.03749 | null | https://arxiv.org/abs/2101.03749v2 | https://arxiv.org/pdf/2101.03749v2.pdf | Deep Learning-based Face Super-Resolution: A Survey | Face super-resolution (FSR), also known as face hallucination, which is aimed at enhancing the resolution of low-resolution (LR) face images to generate high-resolution (HR) face images, is a domain-specific image super-resolution problem. Recently, FSR has received considerable attention and witnessed dazzling advance... | ['Jiayi Ma', 'Xianming Liu', 'Chenyang Wang', 'Junjun Jiang'] | 2021-01-11 | null | null | null | null | ['face-hallucination'] | ['computer-vision'] | [ 2.08047554e-01 -6.02321252e-02 -2.32600391e-01 -4.49138552e-01
-1.04765034e+00 1.03755891e-01 3.61641645e-01 -1.01030874e+00
1.67093247e-01 8.86660397e-01 4.84190792e-01 5.31897426e-01
-9.83567908e-02 -7.21780956e-01 -4.51982409e-01 -6.99063182e-01
-1.15237199e-01 -3.18581462e-02 -5.06838322e-01 -5.18666148... | [12.82116985321045, -0.07869656383991241] |
1537398f-7186-4ea8-8ccd-ea5df3170f6f | a-linguistic-analysis-of-visually-grounded | 2010.03127 | null | https://arxiv.org/abs/2010.03127v1 | https://arxiv.org/pdf/2010.03127v1.pdf | A Linguistic Analysis of Visually Grounded Dialogues Based on Spatial Expressions | Recent models achieve promising results in visually grounded dialogues. However, existing datasets often contain undesirable biases and lack sophisticated linguistic analyses, which make it difficult to understand how well current models recognize their precise linguistic structures. To address this problem, we make tw... | ['Akiko Aizawa', 'Takato Yamazaki', 'Takuma Udagawa'] | 2020-10-07 | null | https://aclanthology.org/2020.findings-emnlp.67 | https://aclanthology.org/2020.findings-emnlp.67.pdf | findings-of-the-association-for-computational | ['spatial-relation-recognition', 'natural-language-visual-grounding'] | ['computer-vision', 'reasoning'] | [ 2.59562545e-02 6.54555023e-01 -1.15945026e-01 -3.67115557e-01
-9.58909631e-01 -1.02589250e+00 9.34617639e-01 1.81850418e-01
-1.96549311e-01 7.84203410e-01 8.81290317e-01 -5.61213374e-01
2.67779768e-01 -5.58501601e-01 -5.59420228e-01 -3.43218625e-01
2.16570958e-01 5.54571390e-01 2.03895196e-01 -6.89621568... | [10.640591621398926, 1.8057893514633179] |
c2050831-72b9-4c26-b2cc-017c03706e23 | dynamic-prediction-of-icu-mortality-risk | 1912.10080 | null | https://arxiv.org/abs/1912.10080v1 | https://arxiv.org/pdf/1912.10080v1.pdf | Dynamic Prediction of ICU Mortality Risk Using Domain Adaptation | Early recognition of risky trajectories during an Intensive Care Unit (ICU) stay is one of the key steps towards improving patient survival. Learning trajectories from physiological signals continuously measured during an ICU stay requires learning time-series features that are robust and discriminative across diverse ... | ['Tiago Alves', 'Nivio Ziviani', 'Alberto Laender', 'Adriano Veloso'] | 2019-12-20 | null | null | null | null | ['icu-mortality'] | ['medical'] | [ 2.43344128e-01 -3.41786683e-01 -1.91701964e-01 -2.50026613e-01
-4.84924734e-01 -3.84747863e-01 2.43534401e-01 9.05701756e-01
-5.41314006e-01 9.21644211e-01 2.77218074e-01 -3.57470453e-01
-7.27061749e-01 -5.35470247e-01 -1.76748589e-01 -9.30708587e-01
-5.18312275e-01 7.04835057e-01 7.07553420e-03 9.15692071... | [8.001042366027832, 6.119062423706055] |
ff0e01be-40c2-4a6a-a532-15f3022b1903 | predicting-motion-plans-for-articulating | 2303.01484 | null | https://arxiv.org/abs/2303.01484v1 | https://arxiv.org/pdf/2303.01484v1.pdf | Predicting Motion Plans for Articulating Everyday Objects | Mobile manipulation tasks such as opening a door, pulling open a drawer, or lifting a toilet lid require constrained motion of the end-effector under environmental and task constraints. This, coupled with partial information in novel environments, makes it challenging to employ classical motion planning approaches at t... | ['Saurabh Gupta', 'Max E. Shepherd', 'Arjun Gupta'] | 2023-03-02 | null | null | null | null | ['motion-prediction', 'motion-planning'] | ['computer-vision', 'robots'] | [ 1.01465881e-01 4.53661196e-02 -1.48016825e-01 9.02476162e-02
-6.69025242e-01 -8.93871665e-01 5.63472867e-01 -1.52620971e-01
-4.16141391e-01 9.68156695e-01 1.32543668e-01 -4.00192171e-01
-4.50332969e-01 -5.99629104e-01 -8.11229169e-01 -1.63759440e-01
-5.86057603e-01 8.97418618e-01 4.69449222e-01 -3.32829356... | [4.581035137176514, 0.8639031648635864] |
20ef3c44-c14c-4efc-9c6b-07bb57fba6ef | snps-filtered-by-allele-frequency-improve-the | 2111.10471 | null | https://arxiv.org/abs/2111.10471v1 | https://arxiv.org/pdf/2111.10471v1.pdf | SNPs Filtered by Allele Frequency Improve the Prediction of Hypertension Subtypes | Hypertension is the leading global cause of cardiovascular disease and premature death. Distinct hypertension subtypes may vary in their prognoses and require different treatments. An individual's risk for hypertension is determined by genetic and environmental factors as well as their interactions. In this work, we st... | ['Yuan Luo', 'Ryan Irvin', 'Donna Arnett', 'Sanjiv J. Shah', 'Yiming Li'] | 2021-11-19 | null | null | null | null | ['epidemiology'] | ['medical'] | [-3.19116414e-02 -2.67362535e-01 -3.48561585e-01 -1.00124526e+00
-1.45716444e-01 -2.37641543e-01 4.79760729e-02 6.89498901e-01
-3.08580905e-01 7.84780979e-01 6.02091491e-01 -5.24823546e-01
-4.45212156e-01 -9.40285742e-01 7.16778859e-02 -3.12073469e-01
-7.29568958e-01 7.55950034e-01 -1.88645825e-01 3.90243270... | [6.677474498748779, 5.542731285095215] |
31e8446f-4c38-46ed-a9a8-a2f008658a70 | learning-from-peers-transfer-reinforcement | 2109.07999 | null | https://arxiv.org/abs/2109.07999v4 | https://arxiv.org/pdf/2109.07999v4.pdf | Learning from Peers: Deep Transfer Reinforcement Learning for Joint Radio and Cache Resource Allocation in 5G RAN Slicing | Network slicing is a critical technique for 5G communications that covers radio access network (RAN), edge, transport and core slicing.The evolving network architecture requires the orchestration of multiple network resources such as radio and cache resources. In recent years, machine learning (ML) techniques have been... | ['Vincent Poor', 'Melike Erol-Kantarci', 'Hao Zhou'] | 2021-09-16 | null | null | null | null | ['transfer-reinforcement-learning'] | ['methodology'] | [-5.70159316e-01 -4.00295928e-02 -8.07943761e-01 -1.57377064e-01
-7.32331812e-01 -2.53234714e-01 2.95657050e-02 -3.89393419e-01
-2.40779266e-01 1.62870395e+00 -6.35667518e-02 -1.04856181e+00
-5.73025167e-01 -9.57400858e-01 -3.02859515e-01 -7.72623718e-01
-4.62772936e-01 7.07581520e-01 2.32375011e-01 -1.13692336... | [5.893185615539551, 1.6761982440948486] |
89be23c2-2a0c-40bc-815b-a7c255185052 | video-background-music-generation-dataset | 2211.11248 | null | https://arxiv.org/abs/2211.11248v1 | https://arxiv.org/pdf/2211.11248v1.pdf | Video Background Music Generation: Dataset, Method and Evaluation | Music is essential when editing videos, but selecting music manually is difficult and time-consuming. Thus, we seek to automatically generate background music tracks given video input. This is a challenging task since it requires plenty of paired videos and music to learn their correspondence. Unfortunately, there exis... | ['Si Liu', 'Xiaobo Li', 'Miao Lu', 'Chenxi Bao', 'Stanley Peng', 'Yue Liao', 'Baisen Wang', 'Zhaokai Wang', 'Le Zhuo'] | 2022-11-21 | null | null | null | null | ['music-generation', 'music-generation'] | ['audio', 'music'] | [ 3.47843468e-01 -7.28489161e-01 -2.80556321e-01 8.37656260e-02
-8.78935456e-01 -7.10026801e-01 5.28237522e-01 -3.12618077e-01
7.84344301e-02 3.46463144e-01 4.08121824e-01 3.50519627e-01
-2.02069908e-01 -3.56029212e-01 -5.35952926e-01 -4.71908122e-01
3.97891887e-02 9.45723280e-02 1.88664779e-01 -1.79590553... | [15.696229934692383, 5.359692573547363] |
04e7c543-3888-49b8-8d22-248ec0d06e4e | urban-traffic-prediction-from-spatio-temporal | null | null | https://www.kdd.org/kdd2019/accepted-papers/view/urban-traffic-prediction-from-spatio-temporal-data-using-deep-meta-learning | https://dl.acm.org/doi/pdf/10.1145/3292500.3330884?download=true | Urban Traffic Prediction from Spatio-Temporal Data Using Deep Meta Learning | Predicting urban traffic is of great importance to intelligent transportation systems and public safety, yet is very challenging because of two aspects: 1) complex spatio-temporal correlations of urban traffic, including spatial correlations between locations along with temporal correlations among timestamps; 2) divers... | ['Yong Yu', 'Zheyi Pan', 'Yu Zheng', 'Weifeng Wang', 'Yuxuan Liang', 'Junbo Zhang'] | 2019-07-25 | null | null | null | kdd-19-2019-7 | ['spatio-temporal-forecasting'] | ['time-series'] | [-8.93012136e-02 -2.44326755e-01 -2.60379702e-01 -3.25352520e-01
-4.21027601e-01 5.20077497e-02 6.50136352e-01 -1.30848303e-01
-2.44259909e-01 7.59688437e-01 4.42337185e-01 -4.68557745e-01
-7.92802945e-02 -1.13918972e+00 -7.65160799e-01 -4.83136743e-01
-2.87913233e-01 2.59403706e-01 6.96894109e-01 -4.82866168... | [6.463883876800537, 2.0647761821746826] |
225f80a2-d3f1-4aec-aa87-5065f4c438af | svde-scalable-value-decomposition-exploration | 2303.09058 | null | https://arxiv.org/abs/2303.09058v1 | https://arxiv.org/pdf/2303.09058v1.pdf | SVDE: Scalable Value-Decomposition Exploration for Cooperative Multi-Agent Reinforcement Learning | Value-decomposition methods, which reduce the difficulty of a multi-agent system by decomposing the joint state-action space into local observation-action spaces, have become popular in cooperative multi-agent reinforcement learning (MARL). However, value-decomposition methods still have the problems of tremendous samp... | ['Xuan Wang', 'Jing Xiao', 'Jiajia Zhang', 'Qiang Wang', 'Shuhao Zhang', 'Shuhan Qi'] | 2023-03-16 | null | null | null | null | ['starcraft-ii', 'starcraft'] | ['playing-games', 'playing-games'] | [-4.52466756e-01 -2.54765123e-01 -2.85472929e-01 1.60522133e-01
-7.99881935e-01 -4.81046021e-01 6.41122580e-01 6.35000989e-02
-7.95254946e-01 1.04210365e+00 1.68740585e-01 -2.77482629e-01
-3.05815756e-01 -8.24806035e-01 -4.91113007e-01 -1.05472088e+00
-4.74726677e-01 4.98365521e-01 3.05117190e-01 -5.39830744... | [3.8674399852752686, 2.01558256149292] |
a6d5c644-457b-40ba-8b44-842710871618 | learning-to-discriminate-information-for | 1912.04461 | null | https://arxiv.org/abs/1912.04461v3 | https://arxiv.org/pdf/1912.04461v3.pdf | Learning to Discriminate Information for Online Action Detection | From a streaming video, online action detection aims to identify actions in the present. For this task, previous methods use recurrent networks to model the temporal sequence of current action frames. However, these methods overlook the fact that an input image sequence includes background and irrelevant actions as wel... | ['Chanho Jung', 'Jinyoung Moon', 'Hyunjun Eun', 'Changick Kim', 'Jongyoul Park'] | 2019-12-10 | learning-to-discriminate-information-for-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Eun_Learning_to_Discriminate_Information_for_Online_Action_Detection_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Eun_Learning_to_Discriminate_Information_for_Online_Action_Detection_CVPR_2020_paper.pdf | cvpr-2020-6 | ['online-action-detection'] | ['computer-vision'] | [ 8.77192974e-01 -2.54845828e-01 -5.79253137e-01 -1.48474686e-02
-5.89963853e-01 -2.97932774e-01 5.37458956e-01 -3.92384417e-02
-5.54408550e-01 4.14929748e-01 5.77395260e-01 3.19970609e-03
1.83982104e-01 -3.91781211e-01 -4.31676388e-01 -7.59167910e-01
-2.31437504e-01 -2.30531633e-01 7.41341233e-01 9.09765884... | [8.38482666015625, 0.5249310731887817] |
63f25a79-49eb-42ab-9204-4c9850ac1c24 | class-specific-semantic-reconstruction-for | 2207.02158 | null | https://arxiv.org/abs/2207.02158v1 | https://arxiv.org/pdf/2207.02158v1.pdf | Class-Specific Semantic Reconstruction for Open Set Recognition | Open set recognition enables deep neural networks (DNNs) to identify samples of unknown classes, while maintaining high classification accuracy on samples of known classes. Existing methods basing on auto-encoder (AE) and prototype learning show great potential in handling this challenging task. In this study, we propo... | ['Ming-Ming Cheng', 'QinGhua Hu', 'Yu Wang', 'Hongzhi Huang'] | 2022-07-05 | null | null | null | null | ['open-set-learning'] | ['miscellaneous'] | [ 1.92870617e-01 -8.15081373e-02 -1.15873225e-01 -5.80694199e-01
-8.12753022e-01 -5.39344132e-01 4.00493532e-01 -3.91490310e-01
-1.26252294e-01 4.57692862e-01 -1.18634954e-01 4.54522930e-02
-9.76963788e-02 -7.91062176e-01 -8.75190854e-01 -5.56659520e-01
1.87932014e-01 3.58012140e-01 -2.04643935e-01 8.18747282... | [9.696627616882324, 2.849518299102783] |
9797abaa-a562-4d40-ac4b-435938fb674e | retinal-image-restoration-using-transformer | 2303.01939 | null | https://arxiv.org/abs/2303.01939v1 | https://arxiv.org/pdf/2303.01939v1.pdf | Retinal Image Restoration using Transformer and Cycle-Consistent Generative Adversarial Network | Medical imaging plays a significant role in detecting and treating various diseases. However, these images often happen to be of too poor quality, leading to decreased efficiency, extra expenses, and even incorrect diagnoses. Therefore, we propose a retinal image enhancement method using a vision transformer and convol... | ['Md Baharul Islam', 'Alnur Alimanov'] | 2023-03-03 | null | null | null | null | ['image-enhancement'] | ['computer-vision'] | [ 5.18105626e-01 -1.07797217e-02 4.42894429e-01 -5.74259683e-02
-5.30962467e-01 -5.21548927e-01 2.49654010e-01 -3.93198282e-01
-4.26282287e-01 9.42025542e-01 9.12538916e-02 -3.02541286e-01
1.80687547e-01 -8.71449709e-01 -6.44771278e-01 -9.96029794e-01
2.50852078e-01 -4.77454692e-01 2.98807919e-01 4.30464335... | [13.361176490783691, -2.3038859367370605] |
928b45c0-c4cc-4002-985e-af9257e78ebf | scida-self-correction-integrated-domain | 2108.06810 | null | https://arxiv.org/abs/2108.06810v2 | https://arxiv.org/pdf/2108.06810v2.pdf | SCIDA: Self-Correction Integrated Domain Adaptation from Single- to Multi-label Aerial Images | Most publicly available datasets for image classification are with single labels, while images are inherently multi-labeled in our daily life. Such an annotation gap makes many pre-trained single-label classification models fail in practical scenarios. This annotation issue is more concerned for aerial images: Aerial d... | ['Z. Jane Wang', 'Xiaoxiang Zhu', 'Yuansheng Hua', 'Lichao Mou', 'Jianzhe Lin', 'Tianze Yu'] | 2021-08-15 | null | null | null | null | ['multi-label-image-classification'] | ['computer-vision'] | [ 6.42724454e-01 -3.23742628e-01 -5.40938735e-01 -5.43928266e-01
-8.53630006e-01 -9.50597227e-01 3.20505828e-01 3.98433030e-01
-3.53716373e-01 6.15651071e-01 -4.84318644e-01 -1.05108261e-01
-3.91265489e-02 -7.63693094e-01 -5.70529222e-01 -7.46671319e-01
2.44989857e-01 3.69017780e-01 2.06956446e-01 2.07547680... | [9.589747428894043, 3.905954122543335] |
56dfef47-5d1d-4f8c-9e00-b3abdb0bfd72 | coherent-multi-sentence-video-description | 1403.6173 | null | http://arxiv.org/abs/1403.6173v1 | http://arxiv.org/pdf/1403.6173v1.pdf | Coherent Multi-Sentence Video Description with Variable Level of Detail | Humans can easily describe what they see in a coherent way and at varying
level of detail. However, existing approaches for automatic video description
are mainly focused on single sentence generation and produce descriptions at a
fixed level of detail. In this paper, we address both of these limitations: for
a variabl... | ['Marcus Rohrbach', 'Anna Senina', 'Wei Qiu', 'Manfred Pinkal', 'Annemarie Friedrich', 'Mykhaylo Andriluka', 'Sikandar Amin', 'Bernt Schiele'] | 2014-03-24 | null | null | null | null | ['video-description'] | ['computer-vision'] | [ 3.75401199e-01 3.56351659e-02 -1.41701117e-01 -7.31321275e-01
-9.20637190e-01 -7.29117632e-01 9.06383216e-01 3.38941693e-01
-3.76311280e-02 7.59378850e-01 6.50161862e-01 4.24024999e-01
1.50451168e-01 -3.36367279e-01 -5.50425470e-01 -1.71632200e-01
2.48018101e-01 3.48258615e-01 3.49044502e-01 -1.08543798... | [10.648809432983398, 0.7587352395057678] |
50440894-ee4e-4a1e-b847-7654aa40baaa | disguisenet-a-contrastive-approach-for | 1804.09669 | null | http://arxiv.org/abs/1804.09669v2 | http://arxiv.org/pdf/1804.09669v2.pdf | DisguiseNet : A Contrastive Approach for Disguised Face Verification in the Wild | This paper describes our approach for the Disguised Faces in the Wild (DFW)
2018 challenge. The task here is to verify the identity of a person among
disguised and impostors images. Given the importance of the task of face
verification it is essential to compare methods across a common platform. Our
approach is based o... | ['Abhinav Dhall', 'Skand Vishwanath Peri'] | 2018-04-25 | null | null | null | null | ['disguised-face-verification'] | ['computer-vision'] | [-1.08422125e-02 6.29936531e-02 9.90887210e-02 -6.49112880e-01
-6.15306973e-01 -7.48369515e-01 8.13365161e-01 -5.11781156e-01
-5.16981781e-01 5.58582485e-01 1.39358759e-01 -1.09963775e-01
9.16518793e-02 -4.82596606e-01 -5.68979919e-01 -5.60222745e-01
-2.24686891e-01 4.99858074e-02 -1.57716945e-01 -2.37340704... | [13.073591232299805, 0.9126225709915161] |
1997f575-3485-46db-8264-20478d1eb033 | robust-automatic-whole-brain-extraction-on | 2006.02627 | null | https://arxiv.org/abs/2006.02627v1 | https://arxiv.org/pdf/2006.02627v1.pdf | Robust Automatic Whole Brain Extraction on Magnetic Resonance Imaging of Brain Tumor Patients using Dense-Vnet | Whole brain extraction, also known as skull stripping, is a process in neuroimaging in which non-brain tissue such as skull, eyeballs, skin, etc. are removed from neuroimages. Skull striping is a preliminary step in presurgical planning, cortical reconstruction, and automatic tumor segmentation. Despite a plethora of s... | ['Kristin R. Swanson', 'J. Ross Mitchell', 'Leland S. Hu', 'Kyle W. Singleton', 'Sara Ranjbar', 'Lisa E. Paulson', 'Lee Curtin', 'Cassandra R. Rickertsen'] | 2020-06-04 | null | null | null | null | ['skull-stripping'] | ['medical'] | [ 2.57497340e-01 4.72400993e-01 2.90065825e-01 -5.04497528e-01
-7.64194071e-01 -3.56828690e-01 4.19122189e-01 1.53630719e-01
-7.94981182e-01 8.84395778e-01 1.20852210e-01 -4.32023585e-01
-1.93456009e-01 -4.33529019e-01 -3.10287029e-01 -8.50862265e-01
-1.45659119e-01 8.44794393e-01 3.41483653e-01 4.97003138... | [14.444437980651855, -2.3771023750305176] |
c4b16286-b0f3-49ff-9190-d9214cb5afda | seeknet-improved-human-instance-segmentation | 2011.08682 | null | https://arxiv.org/abs/2011.08682v2 | https://arxiv.org/pdf/2011.08682v2.pdf | SeekNet: Improved Human Instance Segmentation and Tracking via Reinforcement Learning Based Optimized Robot Relocation | Amodal recognition is the ability of the system to detect occluded objects. Most SOTA Visual Recognition systems lack the ability to perform amodal recognition. Few studies have achieved amodal recognition through passive prediction or embodied recognition approaches. However, these approaches suffer from challenges in... | ['Aniket Bera', 'Phu Pham', 'Rama Prashanth RV', 'Bala Murali Manoghar', 'Venkatraman Narayanan'] | 2020-11-17 | null | null | null | null | ['human-instance-segmentation'] | ['computer-vision'] | [ 1.71323642e-01 -1.12729356e-01 1.07706457e-01 -1.67537704e-02
-5.93274176e-01 -4.07856524e-01 7.31142521e-01 -2.17777893e-01
-5.34359574e-01 8.63139749e-01 5.24857193e-02 -5.15477248e-02
1.34799078e-01 -4.05440569e-01 -8.40877652e-01 -6.81697965e-01
-3.53292704e-01 4.07658964e-01 2.77830333e-01 -1.23169623... | [4.678849220275879, 0.6779903769493103] |
ee9f36e6-3694-4c72-9c3e-c3328a1a8f2c | machine-learning-aided-anonymization-of | 1902.08934 | null | https://arxiv.org/abs/1902.08934v2 | https://arxiv.org/pdf/1902.08934v2.pdf | Privacy Preserving Location Data Publishing: A Machine Learning Approach | Publishing datasets plays an essential role in open data research and promoting transparency of government agencies. However, such data publication might reveal users' private information. One of the most sensitive sources of data is spatiotemporal trajectory datasets. Unfortunately, merely removing unique identifiers ... | ['Sina Shaham', 'Zihuai Lin', 'Jun Li', 'Ming Ding', 'Bo Liu', 'Shuping Dang'] | 2019-02-24 | null | null | null | null | ['multiple-sequence-alignment'] | ['medical'] | [ 7.70252664e-03 -8.16864595e-02 -1.94697991e-01 -4.34800208e-01
-6.85620785e-01 -1.01816344e+00 4.15093333e-01 5.04705071e-01
-6.50090635e-01 9.09529388e-01 2.94327855e-01 -3.40928137e-01
-4.18096721e-01 -1.11143994e+00 -8.31887305e-01 -7.77819395e-01
1.01533683e-03 2.10782260e-01 8.41597244e-02 3.44600976... | [6.08374547958374, 6.782860279083252] |
819629ed-60cd-482c-a6fb-abeb97bfaf1d | sleepppg-net-a-deep-learning-algorithm-for | 2202.05735 | null | https://arxiv.org/abs/2202.05735v4 | https://arxiv.org/pdf/2202.05735v4.pdf | SleepPPG-Net: a deep learning algorithm for robust sleep staging from continuous photoplethysmography | Introduction: Sleep staging is an essential component in the diagnosis of sleep disorders and management of sleep health. It is traditionally measured in a clinical setting and requires a labor-intensive labeling process. We hypothesize that it is possible to perform robust 4-class sleep staging using the raw photoplet... | ['Joachim A. Behar', 'Lea Amar', 'Amir Landesberg', 'Sharon Salabi', 'Peter H. Charlton', 'Kevin Kotzen'] | 2022-02-11 | null | null | null | null | ['photoplethysmography-ppg', 'sleep-staging'] | ['medical', 'medical'] | [ 3.48539394e-03 -1.72441476e-03 -3.05434763e-01 -6.34455264e-01
-6.70402348e-01 -1.60516083e-01 -2.79018044e-01 -1.34081125e-01
-5.66328645e-01 4.01899844e-01 1.59398451e-01 -4.03711826e-01
6.62725940e-02 -2.74222374e-01 1.22566950e-02 -6.38610601e-01
-4.06609327e-01 3.39049071e-01 -9.00472328e-03 1.80140734... | [13.532122611999512, 3.4940438270568848] |
20e09c55-def2-48a8-accb-3e9968cee543 | morpheme-boundary-detection-grammatical | 2112.09860 | null | https://arxiv.org/abs/2112.09860v1 | https://arxiv.org/pdf/2112.09860v1.pdf | Morpheme Boundary Detection & Grammatical Feature Prediction for Gujarati : Dataset & Model | Developing Natural Language Processing resources for a low resource language is a challenging but essential task. In this paper, we present a Morphological Analyzer for Gujarati. We have used a Bi-Directional LSTM based approach to perform morpheme boundary detection and grammatical feature tagging. We have created a d... | ['Dr. Brijesh Bhatt', 'Jatayu Baxi'] | 2021-12-18 | null | https://aclanthology.org/2021.icon-main.45 | https://aclanthology.org/2021.icon-main.45.pdf | icon-2021-12 | ['boundary-detection'] | ['computer-vision'] | [-2.56276806e-04 1.30604282e-01 3.20516616e-01 -3.08799088e-01
-6.02513015e-01 -8.39001715e-01 -2.50394903e-02 7.45374382e-01
-9.32841122e-01 5.70910454e-01 7.96122104e-02 -7.90370703e-01
5.41602820e-02 -1.03848505e+00 -3.55223298e-01 -3.68390083e-01
-2.11831808e-01 6.47903860e-01 -1.22418642e-01 -3.33714128... | [10.38704776763916, 10.115991592407227] |
5d284459-371f-4512-9472-cbd5de34c6d8 | deconfounded-and-explainable-interactive | null | null | https://dl.acm.org/doi/abs/10.1145/3474085.3475366?casa_token=vPQdimvIM_0AAAAA:v79z37V9qYI2Z8QpWndZfLCpcbC6Z2j4LRpmDU8OEdqSET23vzR1gbkr7TGMbgjMGsErTzJU7J-uqrA | https://dl.acm.org/doi/pdf/10.1145/3474085.3475366?casa_token=QelAAj4QhBoAAAAA:l4fJpTytXhwrUZjpHeVzPpk6425a5rs6Sl9Lwys0dw5u9WrMuV_gMd7stznvlP_Ow2FOE79VuXyCYxI | Deconfounded and Explainable Interactive Vision-Language Retrieval of Complex Scenes | In vision-language retrieval systems, users provide natural language feedback to find target images. Vision-language explanations in the systems can better guide users to provide feedback and thus improve the retrieval. However, developing explainable vision-language retrieval systems can be challenging, due to limited... | ['Shuai Li', 'Tong Yu', 'Junda Wu'] | 2021-10-17 | null | null | null | the-29th-acm-international-conference-on | ['explainable-models'] | ['computer-vision'] | [-7.18697235e-02 7.79902115e-02 -5.81313431e-01 -4.87246364e-01
-6.02612078e-01 -4.23465312e-01 4.69487429e-01 -4.65212166e-02
-2.21280843e-01 5.34595788e-01 2.75005639e-01 -2.04576179e-01
-1.02551691e-01 -4.83774155e-01 -7.39839137e-01 -3.25198919e-01
4.02987182e-01 5.18382311e-01 4.16241027e-02 -1.26322880... | [10.768758773803711, 1.738788366317749] |
3249d170-f2ec-424d-bd80-a3c7903f3376 | semeval-2021-task-5-toxic-spans-detection | null | null | https://aclanthology.org/2021.semeval-1.6 | https://aclanthology.org/2021.semeval-1.6.pdf | SemEval-2021 Task 5: Toxic Spans Detection | The Toxic Spans Detection task of SemEval-2021 required participants to predict the spans of toxic posts that were responsible for the toxic label of the posts. The task could be addressed as supervised sequence labeling, using training data with gold toxic spans provided by the organisers. It could also be treated as ... | ['Ion Androutsopoulos', "L{\\'e}o Laugier", 'Jeffrey Sorensen', 'John Pavlopoulos'] | 2021-08-01 | null | null | null | semeval-2021 | ['toxic-spans-detection'] | ['natural-language-processing'] | [ 6.48985863e-01 6.36841416e-01 -3.02907936e-02 -2.41013557e-01
-1.22884178e+00 -1.18124640e+00 4.05444801e-01 9.15145993e-01
-7.73137450e-01 1.25758934e+00 7.02616692e-01 -1.92576587e-01
1.62105232e-01 -4.92863417e-01 -5.61988652e-01 -3.38809878e-01
1.60347760e-01 5.83990991e-01 3.83940578e-01 -1.05551682... | [8.94597339630127, 10.635104179382324] |
b0ecbd6d-b3d4-4e4e-96dc-4041666eb617 | m2pht-mixed-models-with-preferences-and | 2004.01646 | null | https://arxiv.org/abs/2004.01646v4 | https://arxiv.org/pdf/2004.01646v4.pdf | M2: Mixed Models with Preferences, Popularities and Transitions for Next-Basket Recommendation | Next-basket recommendation considers the problem of recommending a set of items into the next basket that users will purchase as a whole. In this paper, we develop a novel mixed model with preferences, popularities and transitions (M2) for the next-basket recommendation. This method models three important factors in ne... | ['Srinivasan Parthasarathy', 'Bo Peng', 'Zhiyun Ren', 'Xia Ning'] | 2020-04-03 | null | null | null | null | ['next-basket-recommendation'] | ['miscellaneous'] | [-9.81398299e-02 -5.33600748e-01 -8.75675976e-01 -3.43346626e-01
-6.48964822e-01 -4.52328175e-01 3.58523041e-01 8.56084526e-02
-1.58759877e-01 5.26196480e-01 9.60831285e-01 -5.43813944e-01
-9.20778513e-02 -9.73939776e-01 -8.87305617e-01 -3.07567805e-01
-2.20956266e-01 3.47314805e-01 7.30736628e-02 -5.80427766... | [10.112464904785156, 5.655359268188477] |
c95bac33-88b6-4af7-968a-9fdf6eec5447 | nne-a-dataset-for-nested-named-entity | 1906.01359 | null | https://arxiv.org/abs/1906.01359v1 | https://arxiv.org/pdf/1906.01359v1.pdf | NNE: A Dataset for Nested Named Entity Recognition in English Newswire | Named entity recognition (NER) is widely used in natural language processing applications and downstream tasks. However, most NER tools target flat annotation from popular datasets, eschewing the semantic information available in nested entity mentions. We describe NNE---a fine-grained, nested named entity dataset over... | ['Ben Hachey', 'Nicky Ringland', 'James R. Curran', 'Xiang Dai', 'Sarvnaz Karimi', 'Cecile Paris'] | 2019-06-04 | null | https://aclanthology.org/P19-1510 | https://aclanthology.org/P19-1510.pdf | acl-2019-7 | ['nested-named-entity-recognition'] | ['natural-language-processing'] | [-5.91490328e-01 4.82307196e-01 -4.24796283e-01 -7.82576799e-01
-1.00569618e+00 -9.75257218e-01 2.44511232e-01 6.53695583e-01
-1.13640380e+00 1.07651973e+00 9.84811246e-01 -2.88687944e-01
3.44001502e-01 -8.70749891e-01 -4.88497376e-01 2.66905986e-02
-2.41895705e-01 3.64532232e-01 5.12987733e-01 -3.40330333... | [9.743061065673828, 9.611786842346191] |
1216d0e3-447f-4bcd-944f-5d265664ea0b | holoface-augmenting-human-to-human | 1802.00278 | null | http://arxiv.org/abs/1802.00278v1 | http://arxiv.org/pdf/1802.00278v1.pdf | HoloFace: Augmenting Human-to-Human Interactions on HoloLens | We present HoloFace, an open-source framework for face alignment, head pose
estimation and facial attribute retrieval for Microsoft HoloLens. HoloFace
implements two state-of-the-art face alignment methods which can be used
interchangeably: one running locally and one running on a remote backend. Head
pose estimation i... | ['Zbigniew Nasarzewski', 'Piotr Garbat', 'Marek Kowalski', 'Grzegorz Galinski'] | 2018-02-01 | null | null | null | null | ['head-pose-estimation'] | ['computer-vision'] | [-3.65664750e-01 1.91380620e-01 1.66074932e-01 -7.27514744e-01
-4.15245265e-01 -5.27141035e-01 5.04612684e-01 -3.02755058e-01
-1.98610902e-01 2.30194986e-01 2.32601851e-01 2.62414068e-01
2.37829193e-01 -2.23435387e-01 -2.23788962e-01 -6.76357031e-01
1.41782714e-02 7.81160474e-01 -2.44597524e-01 -1.17437549... | [13.575535774230957, 0.1614614874124527] |
112fcb0b-5956-42bb-9a83-c0e361841699 | word-embedding-based-antonym-detection-using | null | null | https://aclanthology.info/papers/N15-1100/n15-1100 | https://www.aclweb.org/anthology/N15-1100 | Word Embedding-based Antonym Detection using Thesauri and Distributional Information | null | ['Makoto Miwa', 'Yutaka Sasaki', 'Masataka Ono'] | 2015-05-01 | null | null | null | hlt-2015-5 | ['learning-word-embeddings'] | ['methodology'] | [-2.44508207e-01 3.89024585e-01 -2.65282035e-01 -2.15905145e-01
-8.60921741e-02 -7.76765764e-01 4.48510379e-01 -7.23253429e-01
-5.48377395e-01 1.31954515e+00 3.66348401e-02 -9.49533224e-01
-2.40340635e-01 -1.05564880e+00 -8.44053447e-01 -8.75781775e-01
-7.42435038e-01 6.86515033e-01 1.44298598e-01 -6.52004302... | [-1.539153814315796, 15.86916446685791] |
c8212dfb-9a15-40d7-97a0-9ba0e855535d | frozen-clip-models-are-efficient-video | 2208.03550 | null | https://arxiv.org/abs/2208.03550v1 | https://arxiv.org/pdf/2208.03550v1.pdf | Frozen CLIP Models are Efficient Video Learners | Video recognition has been dominated by the end-to-end learning paradigm -- first initializing a video recognition model with weights of a pretrained image model and then conducting end-to-end training on videos. This enables the video network to benefit from the pretrained image model. However, this requires substanti... | ['Hongsheng Li', 'Yu Qiao', 'Jifeng Dai', 'Xiaogang Wang', 'Gerard de Melo', 'Peng Gao', 'Renrui Zhang', 'Shijie Geng', 'Ziyi Lin'] | 2022-08-06 | null | null | null | null | ['video-recognition', 'action-classification'] | ['computer-vision', 'computer-vision'] | [ 3.18588108e-01 -1.86344266e-01 -4.33640838e-01 -4.82670963e-01
-1.01070321e+00 -4.58941013e-01 4.48727310e-01 -5.72576702e-01
-5.60820162e-01 2.91196644e-01 1.70232102e-01 -8.46966580e-02
2.61417538e-01 -4.37299967e-01 -1.38912916e+00 -6.83328688e-01
-5.89124449e-02 1.48105592e-01 2.27091074e-01 1.90201327... | [9.360779762268066, 0.7698975801467896] |
d545c706-4ec5-4509-ae27-abc57cdcfed6 | multi-band-wi-fi-sensing-with-matched-feature | 2112.14006 | null | https://arxiv.org/abs/2112.14006v2 | https://arxiv.org/pdf/2112.14006v2.pdf | Multi-Band Wi-Fi Sensing with Matched Feature Granularity | Complementary to the fine-grained channel state information (CSI) from the physical layer and coarse-grained received signal strength indicator (RSSI) measurements, the mid-grained spatial beam attributes (e.g., beam SNR) that are available at millimeter-wave (mmWave) bands during the mandatory beam training phase can ... | ['R. Michael Buehrer', 'Philip V. Orlik', 'Ye Wang', 'Toshiaki Koike-Akino', 'Wang', 'Pu', 'Jianyuan Yu'] | 2021-12-28 | null | null | null | null | ['indoor-localization'] | ['computer-vision'] | [ 4.94534165e-01 -4.47531268e-02 -9.24065933e-02 -5.10311365e-01
-1.43630254e+00 -3.42793941e-01 1.88447773e-01 -2.91457385e-01
-3.21531057e-01 8.19402397e-01 4.54007715e-01 -3.52982789e-01
-7.71688640e-01 -1.17375219e+00 -7.89857507e-01 -9.94898975e-01
-2.05336213e-01 3.65810916e-02 -6.73548207e-02 3.63334939... | [6.491450786590576, 0.8629348278045654] |
44e74733-a37c-4c88-97e2-d72e555710b0 | fully-non-homogeneous-atmospheric-scattering | 2108.11292 | null | https://arxiv.org/abs/2108.11292v1 | https://arxiv.org/pdf/2108.11292v1.pdf | Fully Non-Homogeneous Atmospheric Scattering Modeling with Convolutional Neural Networks for Single Image Dehazing | In recent years, single image dehazing models (SIDM) based on atmospheric scattering model (ASM) have achieved remarkable results. However, it is noted that ASM-based SIDM degrades its performance in dehazing real world hazy images due to the limited modelling ability of ASM where the atmospheric light factor (ALF) and... | ['Yong Xu', 'Yuexian Zou', 'Yan Huang', 'Cong Wang'] | 2021-08-25 | null | null | null | null | ['image-dehazing'] | ['computer-vision'] | [ 2.35844880e-01 -3.82548183e-01 6.91272855e-01 -3.20167571e-01
-2.68128157e-01 2.02338368e-01 4.14453983e-01 -4.03587967e-01
-3.49355757e-01 4.37590539e-01 -1.48310838e-02 -9.62763801e-02
-8.24987292e-02 -1.01451063e+00 -5.44097722e-01 -1.31765139e+00
2.04293340e-01 -9.25337002e-02 7.18585253e-01 -4.75075006... | [10.916748046875, -3.1463115215301514] |
f0110b13-2d31-4824-a3e0-e84ccfe4db5c | utilizing-relative-event-time-to-enhance | null | null | https://aclanthology.org/2021.emnlp-main.815 | https://aclanthology.org/2021.emnlp-main.815.pdf | Utilizing Relative Event Time to Enhance Event-Event Temporal Relation Extraction | Event time is one of the most important features for event-event temporal relation extraction. However, explicit event time information in text is sparse. For example, only about 20% of event mentions in TimeBank-Dense have event-time links. In this paper, we propose a joint model for event-event temporal relation clas... | ['Heng Ji', 'Haoyang Wen'] | null | null | null | null | emnlp-2021-11 | ['temporal-relation-extraction', 'temporal-relation-classification', 'relation-classification'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-3.02797258e-01 2.44374350e-01 -7.28035271e-01 -6.05995059e-01
-9.59036052e-01 -3.81898165e-01 7.30109811e-01 6.16360247e-01
-5.24334252e-01 7.77191162e-01 5.96536577e-01 -3.59631568e-01
1.09639429e-02 -9.11888003e-01 -6.11857712e-01 -1.41826436e-01
-5.89622259e-01 4.34447587e-01 6.71282411e-01 -1.35146677... | [9.06563663482666, 9.138322830200195] |
ed54d3d9-9da7-4924-b4f7-91967da2b032 | a-deep-generative-model-for-graph-layout | 1904.12225 | null | https://arxiv.org/abs/1904.12225v7 | https://arxiv.org/pdf/1904.12225v7.pdf | A Deep Generative Model for Graph Layout | Different layouts can characterize different aspects of the same graph. Finding a "good" layout of a graph is thus an important task for graph visualization. In practice, users often visualize a graph in multiple layouts by using different methods and varying parameter settings until they find a layout that best suits ... | ['Kwan-Liu Ma', 'Oh-Hyun Kwon'] | 2019-04-27 | null | null | null | null | ['layout-design', '3d-depth-estimation'] | ['computer-vision', 'computer-vision'] | [-1.47761386e-02 1.56331748e-01 2.10635886e-01 -4.85121906e-01
-2.55429745e-01 -8.25987995e-01 4.85648662e-01 4.52097267e-01
2.52172500e-01 3.81751120e-01 2.54630953e-01 -7.09085882e-01
-3.74258757e-02 -9.38406646e-01 -6.19905829e-01 -5.33439636e-01
-2.07148790e-01 6.10032260e-01 -3.62631917e-01 -4.91997376... | [11.260085105895996, -0.0680108591914177] |
17abbd07-6217-43fb-af9a-36660636eac6 | exploring-the-limits-of-out-of-distribution | 2106.03004 | null | https://arxiv.org/abs/2106.03004v3 | https://arxiv.org/pdf/2106.03004v3.pdf | Exploring the Limits of Out-of-Distribution Detection | Near out-of-distribution detection (OOD) is a major challenge for deep neural networks. We demonstrate that large-scale pre-trained transformers can significantly improve the state-of-the-art (SOTA) on a range of near OOD tasks across different data modalities. For instance, on CIFAR-100 vs CIFAR-10 OOD detection, we i... | ['Balaji Lakshminarayanan', 'Jie Ren', 'Stanislav Fort'] | 2021-06-06 | null | http://proceedings.neurips.cc/paper/2021/hash/3941c4358616274ac2436eacf67fae05-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/3941c4358616274ac2436eacf67fae05-Paper.pdf | neurips-2021-12 | ['unsupervised-pre-training'] | ['methodology'] | [ 1.25285566e-01 4.71216887e-02 3.31042707e-01 -2.16643035e-01
-1.11864340e+00 -4.43455935e-01 7.31349528e-01 1.84461713e-01
-5.13702571e-01 -1.43219149e-04 2.27895468e-01 -8.58831406e-02
3.10383052e-01 -2.66447097e-01 -1.17143345e+00 -4.47261482e-01
-2.16337144e-01 3.17265332e-01 4.33818787e-01 2.64691442... | [9.363472938537598, 2.6130616664886475] |
e053ca3a-1177-40f8-88c6-1c393d4b7ff8 | a-tiered-move-making-algorithm-for-general | 1403.6275 | null | http://arxiv.org/abs/1403.6275v1 | http://arxiv.org/pdf/1403.6275v1.pdf | A Tiered Move-making Algorithm for General Non-submodular Pairwise Energies | A large number of problems in computer vision can be modelled as energy
minimization problems in a Markov Random Field (MRF) or Conditional Random
Field (CRF) framework. Graph-cuts based $\alpha$-expansion is a standard
move-making method to minimize the energy functions with sub-modular pairwise
terms. However, certai... | ['Philip H. S. Torr', 'Vibhav Vineet', 'Jonathan Warrell'] | 2014-03-25 | null | null | null | null | ['image-stitching'] | ['computer-vision'] | [ 5.49392879e-01 1.44950554e-01 -1.76501200e-01 -6.00031912e-01
-9.26617980e-01 -4.12860721e-01 3.75932962e-01 3.50650102e-01
-5.82825899e-01 6.50768697e-01 -5.69869697e-01 -2.23326117e-01
-4.24012423e-01 -8.49533677e-01 -8.70750487e-01 -9.75136340e-01
2.26220302e-02 8.55163455e-01 4.77432609e-01 -1.09251283... | [9.409326553344727, 0.14832034707069397] |
a284aa3c-e10c-4052-8eb4-5a93bb10facb | the-role-of-relevance-in-fair-ranking | 2305.05608 | null | https://arxiv.org/abs/2305.05608v2 | https://arxiv.org/pdf/2305.05608v2.pdf | The Role of Relevance in Fair Ranking | Online platforms mediate access to opportunity: relevance-based rankings create and constrain options by allocating exposure to job openings and job candidates in hiring platforms, or sellers in a marketplace. In order to do so responsibly, these socially consequential systems employ various fairness measures and inter... | ['Asia Biega', 'Abigail Z. Jacobs', 'Aparna Balagopalan'] | 2023-05-09 | null | null | null | null | ['open-question'] | ['natural-language-processing'] | [ 3.36479455e-01 3.98021191e-01 -7.22762525e-01 -5.49488783e-01
-6.27568126e-01 -6.94331408e-01 7.55749583e-01 4.49736804e-01
-9.22348380e-01 8.76273930e-01 4.46991891e-01 -9.03845370e-01
-6.77231848e-01 -6.22315705e-01 -2.16270357e-01 5.87926023e-02
5.40951014e-01 2.95499355e-01 -1.93262190e-01 -3.29111993... | [9.065078735351562, 5.611662864685059] |
962ece63-5acd-4be6-8639-4cb7381a44d8 | icdaelst-intensity-controllable-detail | 2306.16846 | null | https://arxiv.org/abs/2306.16846v1 | https://arxiv.org/pdf/2306.16846v1.pdf | ICDaeLST: Intensity-Controllable Detail Attention-enhanced for Lightweight Fast Style Transfer | The mainstream style transfer methods usually use pre-trained deep convolutional neural network (VGG) models as encoders, or use more complex model structures to achieve better style transfer effects. This leads to extremely slow processing speeds for practical tasks due to limited resources or higher resolution image ... | ['Jiang Shi Qi'] | 2023-06-29 | null | null | null | null | ['style-transfer'] | ['computer-vision'] | [ 1.58321053e-01 -1.87489077e-01 -1.94919109e-01 -3.51095110e-01
-3.48966122e-01 -4.98577476e-01 4.49348241e-01 -2.46012226e-01
-3.57846558e-01 4.86027509e-01 2.23358646e-01 -9.99496430e-02
2.33448222e-01 -9.46507156e-01 -6.14053309e-01 -4.87875402e-01
6.08441293e-01 -1.07043505e-01 3.57371986e-01 -3.22186112... | [11.455155372619629, -0.8297778964042664] |
1fb96dd2-71f3-49cc-a7ca-aa0ed855c78a | vector-of-locally-aggregated-word-embeddings | 1902.08850 | null | https://arxiv.org/abs/1902.08850v3 | https://arxiv.org/pdf/1902.08850v3.pdf | Vector of Locally-Aggregated Word Embeddings (VLAWE): A Novel Document-level Representation | In this paper, we propose a novel representation for text documents based on aggregating word embedding vectors into document embeddings. Our approach is inspired by the Vector of Locally-Aggregated Descriptors used for image representation, and it works as follows. First, the word embeddings gathered from a collection... | ['Radu Tudor Ionescu', 'Andrei M. Butnaru'] | 2019-02-23 | vector-of-locally-aggregated-word-embeddings-1 | https://aclanthology.org/N19-1033 | https://aclanthology.org/N19-1033.pdf | naacl-2019-6 | ['subjectivity-analysis'] | ['natural-language-processing'] | [-1.01542647e-03 -3.61925453e-01 -3.78904819e-01 -6.72930852e-02
-7.73063779e-01 -5.85785031e-01 9.87040043e-01 7.30155408e-01
-6.57389462e-01 4.89506833e-02 5.39827704e-01 -9.87371653e-02
-1.45349339e-01 -7.10871935e-01 -2.67802805e-01 -8.51703942e-01
1.14284486e-01 1.64965242e-01 1.57047272e-01 -8.94031599... | [10.463231086730957, 8.465827941894531] |
a201ea8c-0113-40a1-be76-bdfde5d2e018 | video-based-surgical-skills-assessment-using | 2207.02247 | null | https://arxiv.org/abs/2207.02247v1 | https://arxiv.org/pdf/2207.02247v1.pdf | Video-based Surgical Skills Assessment using Long term Tool Tracking | Mastering the technical skills required to perform surgery is an extremely challenging task. Video-based assessment allows surgeons to receive feedback on their technical skills to facilitate learning and development. Currently, this feedback comes primarily from manual video review, which is time-intensive and limits ... | ['Jocelyn Barker', 'Aishani Ataliwala', 'Anshu Gupta', 'Lela DiMonte', 'Ramon Pena', 'Mohammad Hasan Sarhan', 'Mona Fathollahi'] | 2022-07-05 | null | null | null | null | ['skills-assessment'] | ['computer-vision'] | [-3.14107048e-03 -2.42979318e-01 -5.94233274e-01 3.23787808e-01
-1.05465853e+00 -8.59379888e-01 7.31290579e-02 2.56503701e-01
-6.35027468e-01 1.19423836e-01 1.17067009e-01 -4.94653493e-01
-4.97696668e-01 -2.15394706e-01 -2.81432122e-01 -5.84279060e-01
-2.07975611e-01 2.29875669e-01 4.81371522e-01 -2.37004861... | [14.053077697753906, -3.3592941761016846] |
02300535-3234-4c18-8c9e-363362398107 | diva-domain-invariant-variational-autoencoder | null | null | https://openreview.net/forum?id=SkgkEL8FdV | https://openreview.net/pdf?id=SkgkEL8FdV | DIVA: Domain Invariant Variational Autoencoder | We consider the problem of domain generalization, namely, how to learn representations
given data from a set of domains that generalize to data from a previously
unseen domain. We propose the domain invariant VAE (DIVA), a generative
model that tackles this problem by learning three independent latent subspaces,
one fo... | ['Max Welling', 'Christos Louizos', 'Jakub M. Tomczak', 'Maximilian Ilse'] | 2019-03-27 | null | https://openreview.net/forum?id=rJxotpNYPS | https://openreview.net/pdf?id=rJxotpNYPS | iclr-workshop-deepgenstruct-2019 | ['rotated-mnist'] | ['computer-vision'] | [ 3.80567789e-01 2.06652507e-01 -1.83914945e-01 -4.33811486e-01
-6.44882441e-01 -8.95150304e-01 7.65545666e-01 -3.52476873e-02
-1.68963194e-01 1.00984871e+00 5.12340367e-01 -2.16586795e-02
-3.02933097e-01 -3.34646881e-01 -6.53889775e-01 -9.96250689e-01
2.07606271e-01 9.64013517e-01 2.45573208e-01 -1.34401619... | [10.170479774475098, 2.9676923751831055] |
b2d8e096-6be2-4dc2-9c3b-ebf1a788c6ed | punktuator-a-multilingual-punctuation | null | null | https://aclanthology.org/2021.eacl-demos.37 | https://aclanthology.org/2021.eacl-demos.37.pdf | PunKtuator: A Multilingual Punctuation Restoration System for Spoken and Written Text | Text transcripts without punctuation or sentence boundaries are hard to comprehend for both humans and machines. Punctuation marks play a vital role by providing meaning to the sentence and incorrect use or placement of punctuation marks can often alter it. This can impact downstream tasks such as language translation ... | ['Varnith Chordia'] | 2021-04-01 | null | null | null | eacl-2021-2 | ['punctuation-restoration'] | ['natural-language-processing'] | [ 3.71375233e-01 8.75092894e-02 1.08001895e-01 -4.79218811e-01
-1.02366042e+00 -7.57863045e-01 5.68737090e-01 6.75028324e-01
-6.32138729e-01 1.31166422e+00 7.03388274e-01 -5.56874394e-01
8.51066858e-02 -2.65359253e-01 -4.54005152e-01 -1.30187243e-01
4.11457419e-01 6.12189770e-01 -5.97855225e-02 -7.63624549... | [14.111234664916992, 7.254117012023926] |
b00b1d14-ad1d-4778-949e-4f3f9ce06b8f | off-by-one-implementation-error-in-j-uniward | 2305.19776 | null | https://arxiv.org/abs/2305.19776v1 | https://arxiv.org/pdf/2305.19776v1.pdf | Off-By-One Implementation Error in J-UNIWARD | J-UNIWARD is a popular steganography method for hiding secret messages in JPEG cover images. As a content-adaptive method, J-UNIWARD aims to embed into textured image regions where changes are difficult to detect. To this end, J-UNIWARD first assigns to each DCT coefficient an embedding cost calculated based on the ima... | ['Benedikt Lorch'] | 2023-05-31 | null | null | null | null | ['steganalysis'] | ['computer-vision'] | [ 7.42098331e-01 2.47576565e-01 -9.60758105e-02 2.19531246e-02
-5.22036791e-01 -3.17502707e-01 2.64000088e-01 5.26571274e-02
-5.17983973e-01 5.26763976e-01 -8.64431411e-02 -5.36645114e-01
5.38850784e-01 -1.09602845e+00 -8.25368941e-01 -1.04588425e+00
-5.27238727e-01 -4.47737604e-01 6.90842748e-01 -1.16330348... | [4.324151039123535, 8.047843933105469] |
2d76f4fa-436f-4b36-895f-f2ad382a9568 | synthetic-data-based-detection-of-zebras-in | 2305.00432 | null | https://arxiv.org/abs/2305.00432v2 | https://arxiv.org/pdf/2305.00432v2.pdf | Synthetic Data-based Detection of Zebras in Drone Imagery | Nowadays, there is a wide availability of datasets that enable the training of common object detectors or human detectors. These come in the form of labelled real-world images and require either a significant amount of human effort, with a high probability of errors such as missing labels, or very constrained scenarios... | ['Aamir Ahmad', 'Elia Bonetto'] | 2023-04-30 | null | null | null | null | ['synthetic-data-generation', 'synthetic-data-generation'] | ['medical', 'miscellaneous'] | [ 1.42189115e-01 1.26794592e-01 2.68212944e-01 -2.14036897e-01
-5.28670669e-01 -6.83631837e-01 4.12651807e-01 -3.10130063e-02
-5.39415538e-01 6.69599354e-01 -5.92069626e-01 2.44536594e-01
1.29340366e-01 -7.33497441e-01 -1.03953338e+00 -4.12675142e-01
3.33907688e-03 9.68808770e-01 7.44215071e-01 -2.70555168... | [7.631515026092529, -1.0040197372436523] |
6fd3a962-008a-448c-b12f-46c79b315111 | ai-ku-at-semeval-2016-task-11-word-embeddings | null | null | https://aclanthology.org/S16-1163 | https://aclanthology.org/S16-1163.pdf | AI-KU at SemEval-2016 Task 11: Word Embeddings and Substring Features for Complex Word Identification | null | ['Onur Kuru'] | 2016-06-01 | null | null | null | semeval-2016-6 | ['complex-word-identification'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.390381813049316, 3.680804491043091] |
32b22725-7215-49ea-bdfa-d5900ce77fd9 | learning-person-object-interactions-for | null | null | http://papers.nips.cc/paper/4224-learning-person-object-interactions-for-action-recognition-in-still-images | http://papers.nips.cc/paper/4224-learning-person-object-interactions-for-action-recognition-in-still-images.pdf | Learning person-object interactions for action recognition in still images | We investigate a discriminatively trained model of person-object interactions for recognizing common human actions in still images. We build on the locally order-less spatial pyramid bag-of-features model, which was shown to perform extremely well on a range of object, scene and human action recognition tasks. We intro... | ['Ivan Laptev', 'Vincent Delaitre', 'Josef Sivic'] | 2011-12-01 | null | null | null | neurips-2011-12 | ['action-recognition-in-still-images'] | ['computer-vision'] | [ 4.29279178e-01 -1.51857138e-01 -2.56898075e-01 -5.04041255e-01
-6.95858419e-01 -1.75693527e-01 6.36040628e-01 -1.25155658e-01
-3.35421771e-01 4.33784783e-01 7.91291177e-01 5.73458850e-01
-1.50423408e-01 -2.61929929e-01 -7.42391467e-01 -7.10355878e-01
-4.81356800e-01 5.29143453e-01 4.71246451e-01 -2.75124133... | [7.955448627471924, 0.30087828636169434] |
9584540c-2000-4ec5-ab7d-037f848126dc | salsa-lite-a-fast-and-effective-feature-for | 2111.08192 | null | https://arxiv.org/abs/2111.08192v2 | https://arxiv.org/pdf/2111.08192v2.pdf | SALSA-Lite: A Fast and Effective Feature for Polyphonic Sound Event Localization and Detection with Microphone Arrays | Polyphonic sound event localization and detection (SELD) has many practical applications in acoustic sensing and monitoring. However, the development of real-time SELD has been limited by the demanding computational requirement of most recent SELD systems. In this work, we introduce SALSA-Lite, a fast and effective fea... | ['Woon-Seng Gan', 'Huy Phan', 'Karn N. Watcharasupat', 'Douglas L. Jones', 'Thi Ngoc Tho Nguyen'] | 2021-11-16 | null | null | null | null | ['sound-event-localization-and-detection'] | ['audio'] | [ 2.73044646e-01 -7.11855292e-01 7.72508740e-01 -1.51161170e-02
-1.26795411e+00 -4.31367606e-01 1.68078348e-01 1.83620602e-01
-6.26787961e-01 3.98593783e-01 2.25223616e-01 -2.85642520e-02
-4.68677789e-01 -3.76312077e-01 -3.94895047e-01 -9.07897711e-01
-4.63898242e-01 -5.21835744e-01 6.52590334e-01 -1.70248840... | [15.196362495422363, 5.343943119049072] |
7fce0e61-03ab-4c93-936a-e8745bc81ea7 | a-temporally-aware-interpolation-network-for | 1803.07218 | null | http://arxiv.org/abs/1803.07218v2 | http://arxiv.org/pdf/1803.07218v2.pdf | A Temporally-Aware Interpolation Network for Video Frame Inpainting | We propose the first deep learning solution to video frame inpainting, a
challenging instance of the general video inpainting problem with applications
in video editing, manipulation, and forensics. Our task is less ambiguous than
frame interpolation and video prediction because we have access to both the
temporal cont... | ['Ximeng Sun', 'Ryan Szeto', 'Jason J. Corso'] | 2018-03-20 | null | null | null | null | ['video-inpainting'] | ['computer-vision'] | [ 3.03524762e-01 9.80293900e-02 -3.18601906e-01 -4.59014118e-01
-8.57066453e-01 -2.08848760e-01 4.81189013e-01 -2.09929302e-01
-1.78796574e-01 7.30544984e-01 4.85775739e-01 -1.57826737e-01
5.77505708e-01 -4.18103576e-01 -1.17778254e+00 -3.99644256e-01
3.14405747e-02 1.12356462e-01 3.55703473e-01 1.55519709... | [10.708412170410156, -1.0861411094665527] |
d567d0d9-cc1b-4e10-bae0-d9cf462643af | benchmarking-deepart-detection | 2302.14475 | null | https://arxiv.org/abs/2302.14475v1 | https://arxiv.org/pdf/2302.14475v1.pdf | Benchmarking Deepart Detection | Deepfake technologies have been blurring the boundaries between the real and unreal, likely resulting in malicious events. By leveraging newly emerged deepfake technologies, deepfake researchers have been making a great upending to create deepfake artworks (deeparts), which are further closing the gap between reality a... | ['Xiaopeng Hong', 'Zhiwu Huang', 'Yabin Wang'] | 2023-02-28 | null | null | null | null | ['face-swapping'] | ['computer-vision'] | [-5.04763842e-01 -2.69038439e-01 -3.34746353e-02 2.21451186e-02
-2.68311352e-01 -7.60013044e-01 1.19029474e+00 -6.79027557e-01
-8.40858370e-02 6.39901757e-01 2.32956499e-01 -2.54372627e-01
6.61757812e-02 -8.58926058e-01 -5.51643312e-01 -7.24161386e-01
1.95298176e-02 2.75438607e-01 3.39872330e-01 -2.93225884... | [12.472400665283203, 1.0797208547592163] |
6e153c5b-57d7-4d1b-84c6-8e3f29337489 | ev-nerf-event-based-neural-radiance-field | 2206.12455 | null | https://arxiv.org/abs/2206.12455v2 | https://arxiv.org/pdf/2206.12455v2.pdf | Ev-NeRF: Event Based Neural Radiance Field | We present Ev-NeRF, a Neural Radiance Field derived from event data. While event cameras can measure subtle brightness changes in high frame rates, the measurements in low lighting or extreme motion suffer from significant domain discrepancy with complex noise. As a result, the performance of event-based vision tasks d... | ['Young Min Kim', 'Junho Kim', 'Inwoo Hwang'] | 2022-06-24 | null | null | null | null | ['event-based-vision'] | ['computer-vision'] | [ 4.62595463e-01 -2.03165323e-01 3.40945840e-01 -5.54597616e-01
-7.36298561e-01 -2.75930226e-01 2.02902138e-01 -5.42185426e-01
-3.25908184e-01 6.93496227e-01 1.77097932e-01 4.47337776e-01
3.11392732e-03 -6.75690472e-01 -1.10056245e+00 -8.26323748e-01
4.29656476e-01 3.67263407e-02 1.37599662e-01 -5.37217176... | [10.332072257995605, -2.210538148880005] |
529a1782-c88e-402c-9379-a552e5dad057 | the-inception-platform-machine-assisted-and | null | null | https://aclanthology.org/C18-2002 | https://aclanthology.org/C18-2002.pdf | The INCEpTION Platform: Machine-Assisted and Knowledge-Oriented Interactive Annotation | We introduce INCEpTION, a new annotation platform for tasks including interactive and semantic annotation (e.g., concept linking, fact linking, knowledge base population, semantic frame annotation). These tasks are very time consuming and demanding for annotators, especially when knowledge bases are used. We address th... | ['Richard Eckart de Castilho', 'Jan-Christoph Klie', 'Iryna Gurevych', 'Beto Boullosa', 'Michael Bugert'] | 2018-08-01 | the-inception-platform-machine-assisted-and-1 | https://aclanthology.org/C18-2002 | https://aclanthology.org/C18-2002.pdf | coling-2018-8 | ['text-annotation'] | ['natural-language-processing'] | [-1.13020360e-01 9.37431872e-01 -3.36246967e-01 -5.60945496e-02
-4.34516013e-01 -9.86650169e-01 5.55983007e-01 7.50813067e-01
-5.44660866e-01 1.10840213e+00 2.87721395e-01 -2.63325602e-01
-4.80858460e-02 -5.67732930e-01 -2.16865480e-01 -6.70506656e-02
2.52022833e-01 9.29583371e-01 6.41402006e-01 -4.85931993... | [9.269445419311523, 8.772201538085938] |
97ca4cd7-dfb9-4c6e-afd4-b5fa4f4ad1bc | semantic-clustering-of-a-sequence-of | 2208.13504 | null | https://arxiv.org/abs/2208.13504v2 | https://arxiv.org/pdf/2208.13504v2.pdf | Unsupervised Semantic Analysis of a Region from Satellite Image Time Series | Temporal sequences of satellite images constitute a highly valuable and abundant resource to analyze a given region. However, the labeled data needed to train most machine learning models are scarce and difficult to obtain. In this context, the current work investigates a fully unsupervised methodology that, given a se... | ['María Dolores Ugarte', 'Unai Pérez-Goya', 'Guzmán Santafé', 'Aritz Pérez', 'Carlos Echegoyen'] | 2022-08-29 | null | null | null | null | ['temporal-sequences'] | ['reasoning'] | [ 2.21442714e-01 1.07367292e-01 7.65482560e-02 -2.85151184e-01
-1.29581034e-01 -6.54017210e-01 8.78021538e-01 6.77120030e-01
-1.71664327e-01 5.14038622e-01 1.25281662e-01 -7.64606297e-02
-4.12708551e-01 -1.14462364e+00 -4.63428915e-01 -8.93624246e-01
-4.45882618e-01 3.43834639e-01 4.24297005e-01 -2.11732298... | [7.702065944671631, 4.395302772521973] |
4d4d5ae2-6793-4fa6-8a39-33eb549714ef | a-survey-on-recent-advances-and-challenges-in | 2202.13675 | null | https://arxiv.org/abs/2202.13675v2 | https://arxiv.org/pdf/2202.13675v2.pdf | A Survey on Recent Advances and Challenges in Reinforcement Learning Methods for Task-Oriented Dialogue Policy Learning | Dialogue Policy Learning is a key component in a task-oriented dialogue system (TDS) that decides the next action of the system given the dialogue state at each turn. Reinforcement Learning (RL) is commonly chosen to learn the dialogue policy, regarding the user as the environment and the system as the agent. Many benc... | ['Kam-Fai Wong', 'Huimin Wang', 'Hongru Wang', 'Wai-Chung Kwan'] | 2022-02-28 | null | null | null | null | ['dialogue-management'] | ['natural-language-processing'] | [ 5.53882215e-03 6.36379957e-01 -5.02154469e-01 -4.63691056e-01
-4.08126980e-01 -7.68280566e-01 9.93078768e-01 -6.72504753e-02
-4.40412581e-01 1.30821121e+00 5.12420416e-01 -6.38739467e-01
1.52105421e-01 -6.44678175e-01 8.35583508e-02 -6.19933903e-01
4.50204201e-02 7.83695102e-01 4.72994559e-02 -1.01090968... | [13.074150085449219, 8.018310546875] |
d707e826-f857-45bd-a615-7f057a0965c6 | distilled-non-semantic-speech-embeddings-with | 2207.05784 | null | https://arxiv.org/abs/2207.05784v3 | https://arxiv.org/pdf/2207.05784v3.pdf | Distilled Non-Semantic Speech Embeddings with Binary Neural Networks for Low-Resource Devices | This work introduces BRILLsson, a novel binary neural network-based representation learning model for a broad range of non-semantic speech tasks. We train the model with knowledge distillation from a large and real-valued TRILLsson model with only a fraction of the dataset used to train TRILLsson. The resulting BRILLss... | ['Aaqib Saeed', 'Harlin Lee'] | 2022-07-12 | null | null | null | null | ['keyword-spotting', 'spoken-language-identification'] | ['speech', 'speech'] | [ 2.01873824e-01 1.62899345e-01 -3.97741884e-01 -5.37634730e-01
-6.81394994e-01 -1.88498706e-01 4.01219949e-02 1.43286288e-01
-7.05435574e-01 6.73181713e-01 -1.84827652e-02 -4.97920305e-01
4.70190346e-02 -4.01891023e-01 -4.63634372e-01 -3.41580659e-01
-1.32807037e-02 6.17914915e-01 -7.69647956e-02 1.95765030... | [14.265230178833008, 6.352530479431152] |
13605d9e-e6c4-4083-b1e2-e43103ccb222 | topic-analysis-for-text-with-side-data | 2203.00762 | null | https://arxiv.org/abs/2203.00762v1 | https://arxiv.org/pdf/2203.00762v1.pdf | Topic Analysis for Text with Side Data | Although latent factor models (e.g., matrix factorization) obtain good performance in predictions, they suffer from several problems including cold-start, non-transparency, and suboptimal recommendations. In this paper, we employ text with side data to tackle these limitations. We introduce a hybrid generative probabil... | ['Diego Klabjan', 'Kripa Rajshekhar', 'Biyi Fang'] | 2022-03-01 | null | null | null | null | ['comment-generation'] | ['natural-language-processing'] | [-2.64755189e-01 4.67729837e-01 -5.76519966e-01 -5.04892528e-01
-8.61197829e-01 -3.84281635e-01 1.13923311e+00 -7.74844363e-02
1.03865080e-01 7.14506805e-01 7.87213445e-01 -2.25074589e-01
1.02631822e-01 -8.18972349e-01 -4.98698175e-01 -9.63902771e-01
1.94118530e-01 1.06573141e+00 -8.00240263e-02 1.34329930... | [10.374702453613281, 6.939688205718994] |
02bb8d95-944e-4125-954a-cc79d6f4a02d | english-to-chinese-transliteration-with | null | null | https://aclanthology.org/2020.aacl-main.40 | https://aclanthology.org/2020.aacl-main.40.pdf | English-to-Chinese Transliteration with Phonetic Auxiliary Task | Approaching named entities transliteration as a Neural Machine Translation (NMT) problem is common practice. While many have applied various NMT techniques to enhance machine transliteration models, few focus on the linguistic features particular to the relevant languages. In this paper, we investigate the effect of in... | ['Shay B. Cohen', 'Yuan He'] | 2020-12-01 | null | null | null | asian-chapter-of-the-association-for | ['transliteration'] | ['natural-language-processing'] | [ 2.29618713e-01 -2.11512029e-01 -4.77632850e-01 -5.83358526e-01
-1.31534660e+00 -5.52946031e-01 6.77977920e-01 -4.14460301e-01
-6.41415179e-01 9.00618732e-01 4.01806116e-01 -9.34153795e-01
3.65579933e-01 -4.26741302e-01 -8.63525212e-01 -2.92461634e-01
6.54004037e-01 8.45810533e-01 -5.05872257e-02 -2.57651091... | [11.492948532104492, 10.290901184082031] |
575b5f02-37ee-43b7-b261-667e456f52d4 | an-efficient-subpopulation-based-membership | 2203.02080 | null | https://arxiv.org/abs/2203.02080v1 | https://arxiv.org/pdf/2203.02080v1.pdf | An Efficient Subpopulation-based Membership Inference Attack | Membership inference attacks allow a malicious entity to predict whether a sample is used during training of a victim model or not. State-of-the-art membership inference attacks have shown to achieve good accuracy which poses a great privacy threat. However, majority of SOTA attacks require training dozens to hundreds ... | ['Xin Liu', 'Shahbaz Rezaei'] | 2022-03-04 | null | null | null | null | ['membership-inference-attack'] | ['computer-vision'] | [-4.86534499e-02 6.83811605e-02 -5.15824370e-02 -3.07146460e-01
-9.18288708e-01 -1.01916742e+00 5.53335786e-01 9.49843004e-02
-3.38470846e-01 6.43211424e-01 -5.21928072e-01 -6.95755005e-01
2.28283226e-01 -1.24551952e+00 -9.86150324e-01 -7.67589927e-01
-2.89196130e-02 1.01877546e+00 3.65249664e-01 1.30664244... | [5.848245620727539, 7.3303046226501465] |
e3676c44-5061-4fef-980b-5c5a420b7e96 | visual-language-pretrained-multiple-instance-1 | 2306.07831 | null | https://arxiv.org/abs/2306.07831v1 | https://arxiv.org/pdf/2306.07831v1.pdf | Visual Language Pretrained Multiple Instance Zero-Shot Transfer for Histopathology Images | Contrastive visual language pretraining has emerged as a powerful method for either training new language-aware image encoders or augmenting existing pretrained models with zero-shot visual recognition capabilities. However, existing works typically train on large datasets of image-text pairs and have been designed to ... | ['Faisal Mahmood', 'Yung-Sung Chuang', 'Long Phi Le', 'Tong Ding', 'Richard J. Chen', 'Drew F. K. Williamson', 'Andrew Zhang', 'Bowen Chen', 'Ming Y. Lu'] | 2023-06-13 | visual-language-pretrained-multiple-instance | http://openaccess.thecvf.com//content/CVPR2023/html/Lu_Visual_Language_Pretrained_Multiple_Instance_Zero-Shot_Transfer_for_Histopathology_Images_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Lu_Visual_Language_Pretrained_Multiple_Instance_Zero-Shot_Transfer_for_Histopathology_Images_CVPR_2023_paper.pdf | cvpr-2023-1 | ['whole-slide-images', 'multiple-instance-learning'] | ['computer-vision', 'methodology'] | [ 6.55942619e-01 3.46197724e-01 -2.23539740e-01 -2.36041561e-01
-1.63140643e+00 -3.35012525e-01 5.76259792e-01 2.12208271e-01
-7.32156456e-01 7.10105300e-01 1.33706719e-01 -5.95630229e-01
2.40064338e-01 -5.45498490e-01 -9.95986521e-01 -7.92432427e-01
1.16354167e-01 4.57038671e-01 3.56984474e-02 -7.74047598... | [15.035213470458984, -2.073068380355835] |
139fe27f-ad50-4b17-af4f-45eb447fecdf | map-estimation-for-graphical-models-by | null | null | http://papers.nips.cc/paper/4165-map-estimation-for-graphical-models-by-likelihood-maximization | http://papers.nips.cc/paper/4165-map-estimation-for-graphical-models-by-likelihood-maximization.pdf | MAP Estimation for Graphical Models by Likelihood Maximization | Computing a {\em maximum a posteriori} (MAP) assignment in graphical models is a crucial inference problem for many practical applications. Several provably convergent approaches have been successfully developed using linear programming (LP) relaxation of the MAP problem. We present an alternative approach, which trans... | ['Akshat Kumar', 'Shlomo Zilberstein'] | 2010-12-01 | null | null | null | neurips-2010-12 | ['protein-design'] | ['medical'] | [ 4.51149464e-01 5.11796296e-01 -7.82736614e-02 -6.52242184e-01
-1.08813202e+00 -4.70527053e-01 4.03279603e-01 3.09744030e-01
-4.75542873e-01 1.09950697e+00 -3.20887655e-01 -6.99906647e-01
-4.99258935e-01 -8.10765624e-01 -1.04779303e+00 -8.32834661e-01
-1.71691000e-01 1.23236251e+00 1.37127116e-01 3.56192499... | [7.184629440307617, 4.79665994644165] |
3598380c-3419-489a-9505-8269f4c2ac8a | learning-bias-invariant-representation-by | 2108.05449 | null | https://arxiv.org/abs/2108.05449v2 | https://arxiv.org/pdf/2108.05449v2.pdf | Learning Bias-Invariant Representation by Cross-Sample Mutual Information Minimization | Deep learning algorithms mine knowledge from the training data and thus would likely inherit the dataset's bias information. As a result, the obtained model would generalize poorly and even mislead the decision process in real-life applications. We propose to remove the bias information misused by the target task with ... | ['Jiebo Luo', 'Weijian Li', 'Haofu Liao', 'Haitian Zheng', 'Wei Zhu'] | 2021-08-11 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Zhu_Learning_Bias-Invariant_Representation_by_Cross-Sample_Mutual_Information_Minimization_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Zhu_Learning_Bias-Invariant_Representation_by_Cross-Sample_Mutual_Information_Minimization_ICCV_2021_paper.pdf | iccv-2021-1 | ['mutual-information-estimation'] | ['methodology'] | [ 2.81647295e-01 -1.23222783e-01 -3.05719614e-01 -3.26561153e-01
-8.88463616e-01 -5.82233846e-01 7.46766627e-01 -3.07159454e-01
-2.00337470e-01 9.36448872e-01 4.21088964e-01 -2.03054696e-02
-4.95481938e-02 -8.03146541e-01 -8.73471618e-01 -9.87036109e-01
1.82413355e-01 2.32120872e-01 -2.91244149e-01 -6.22215718... | [9.106176376342773, 4.6142578125] |
97c65a8a-d4d3-43e4-ac10-e70a0f157150 | cross-scale-multi-instance-learning-for | 2304.00216 | null | https://arxiv.org/abs/2304.00216v1 | https://arxiv.org/pdf/2304.00216v1.pdf | Cross-scale Multi-instance Learning for Pathological Image Diagnosis | Analyzing high resolution whole slide images (WSIs) with regard to information across multiple scales poses a significant challenge in digital pathology. Multi-instance learning (MIL) is a common solution for working with high resolution images by classifying bags of objects (i.e. sets of smaller image patches). Howeve... | ['Yuankai Huo', 'Bennett A. Landman', 'Lori A. Coburn', 'Yaohong Wang', 'Keith T. Wilson', 'Qi Liu', 'Ken S. Lau', 'Joseph T. Roland', 'Jia Li', 'Sophie Chiron', 'R. Michael Womick', 'Shunxing Bao', 'Lucas W. Remedios', 'Can Cui', 'Ruining Deng'] | 2023-04-01 | null | null | null | null | ['whole-slide-images'] | ['computer-vision'] | [ 5.08983254e-01 2.10765731e-02 -9.47191790e-02 -1.95318550e-01
-1.60083008e+00 -4.26059514e-01 2.28782207e-01 5.48140466e-01
-5.02272666e-01 4.77375448e-01 9.08798799e-02 -5.42371608e-02
-4.11913007e-01 -5.40795684e-01 -5.64990342e-01 -9.07715917e-01
9.97094903e-04 1.86177507e-01 3.04488033e-01 -1.35710463... | [15.093948364257812, -2.8611528873443604] |
bc4df425-557f-4d6b-b31c-ac3706d0c548 | exploiting-weakly-labeled-web-images-to | null | null | http://papers.nips.cc/paper/4064-exploiting-weakly-labeled-web-images-to-improve-object-classification-a-domain-adaptation-approach | http://papers.nips.cc/paper/4064-exploiting-weakly-labeled-web-images-to-improve-object-classification-a-domain-adaptation-approach.pdf | Exploiting weakly-labeled Web images to improve object classification: a domain adaptation approach | Most current image categorization methods require large collections of manually annotated training examples to learn accurate visual recognition models. The time-consuming human labeling effort effectively limits these approaches to recognition problems involving a small number of different object classes. In order to ... | ['Alessandro Bergamo', 'Lorenzo Torresani'] | 2010-12-01 | null | null | null | neurips-2010-12 | ['image-categorization'] | ['computer-vision'] | [ 4.38628852e-01 -9.15017650e-02 -6.24325693e-01 -6.36020243e-01
-1.16441047e+00 -1.02522910e+00 8.00114453e-01 1.17426999e-01
-6.58022940e-01 8.61203969e-01 -3.57225507e-01 -1.30887225e-01
1.58421740e-01 -5.28118551e-01 -8.90903592e-01 -6.16559863e-01
2.14707747e-01 7.80173838e-01 5.57563961e-01 1.84238836... | [9.685806274414062, 2.488971710205078] |
8e70c10a-3dbb-4f3a-b51f-9b014025e588 | adaptive-recursive-circle-framework-for-fine | 2107.11813 | null | https://arxiv.org/abs/2107.11813v1 | https://arxiv.org/pdf/2107.11813v1.pdf | Adaptive Recursive Circle Framework for Fine-grained Action Recognition | How to model fine-grained spatial-temporal dynamics in videos has been a challenging problem for action recognition. It requires learning deep and rich features with superior distinctiveness for the subtle and abstract motions. Most existing methods generate features of a layer in a pure feedforward manner, where the i... | ['Jiebo Luo', 'Xinxiao wu', 'Hanxi Lin'] | 2021-07-25 | null | null | null | null | ['fine-grained-action-recognition'] | ['computer-vision'] | [ 3.07682723e-01 -4.60345626e-01 -3.34566504e-01 -2.74845630e-01
-1.12246953e-01 -2.97365665e-01 6.34192526e-01 -4.59973097e-01
-5.18889964e-01 7.43192017e-01 6.31622314e-01 9.74348485e-02
-5.79404924e-03 -6.53931856e-01 -7.09013760e-01 -8.85844588e-01
-1.54062063e-01 4.68540341e-02 6.94653273e-01 -3.52096647... | [8.532564163208008, 0.47101110219955444] |
c026a901-5ce2-483c-8781-3613505f579e | real-time-semantic-scene-completion-via | 2303.10967 | null | https://arxiv.org/abs/2303.10967v2 | https://arxiv.org/pdf/2303.10967v2.pdf | Real-time 3D Semantic Scene Completion Via Feature Aggregation and Conditioned Prediction | Semantic Scene Completion (SSC) aims to simultaneously predict the volumetric occupancy and semantic category of a 3D scene. In this paper, we propose a real-time semantic scene completion method with a feature aggregation strategy and conditioned prediction module. Feature aggregation fuses feature with different rece... | ['Gang Zeng', 'Yajie Xing', 'Xiaokang Chen'] | 2023-03-20 | null | null | null | null | ['3d-semantic-scene-completion'] | ['computer-vision'] | [ 3.01339358e-01 -3.53268385e-01 1.03853196e-01 -6.13761008e-01
-5.11222482e-01 -1.51908211e-03 5.29650509e-01 2.05202997e-01
-3.06632996e-01 3.24468255e-01 2.81930119e-01 -1.48457304e-01
2.10915804e-01 -8.93876910e-01 -5.02289474e-01 -4.08460051e-01
1.28147274e-01 2.49876782e-01 7.67137408e-01 1.17406569... | [8.421076774597168, -2.840156078338623] |
a05b8d49-348a-439d-a4ad-6607f9a6ef74 | rethinking-prl-a-multiscale-progressively | 2305.17355 | null | https://arxiv.org/abs/2305.17355v1 | https://arxiv.org/pdf/2305.17355v1.pdf | Rethinking PRL: A Multiscale Progressively Residual Learning Network for Inverse Halftoning | Image inverse halftoning is a classic image restoration task, aiming to recover continuous-tone images from halftone images with only bilevel pixels. Because the halftone images lose much of the original image content, inverse halftoning is a classic ill-problem. Although existing inverse halftoning algorithms achieve ... | ['Jun Yang', 'Feiyu Li'] | 2023-05-27 | null | null | null | null | ['image-restoration'] | ['computer-vision'] | [ 5.33532023e-01 -3.84470552e-01 -2.06099469e-02 -2.71248877e-01
-7.73551643e-01 -6.18338101e-02 3.12299043e-01 -7.23484159e-01
-3.15341532e-01 3.63103718e-01 2.53887922e-01 -1.09130785e-01
1.50023401e-01 -6.96001053e-01 -6.93420827e-01 -7.59257078e-01
3.39639455e-01 -2.68294126e-01 3.88308555e-01 -4.81415629... | [11.062786102294922, -2.2099034786224365] |
c84c63de-c138-43ed-8f55-023dddf26659 | visual-semantic-segmentation-based-on-few | 2211.08352 | null | https://arxiv.org/abs/2211.08352v1 | https://arxiv.org/pdf/2211.08352v1.pdf | Visual Semantic Segmentation Based on Few/Zero-Shot Learning: An Overview | Visual semantic segmentation aims at separating a visual sample into diverse blocks with specific semantic attributes and identifying the category for each block, and it plays a crucial role in environmental perception. Conventional learning-based visual semantic segmentation approaches count heavily on large-scale tra... | ['Qing-Long Han', 'Chaoqiang Zhao', 'Qiyu Sun', 'Yang Tang', 'Wenqi Ren'] | 2022-11-13 | null | null | null | null | ['video-object-segmentation'] | ['computer-vision'] | [ 3.90973687e-01 3.94569933e-02 -6.54801607e-01 -4.05314863e-01
-6.36121452e-01 -6.59283698e-01 4.00848866e-01 1.19968548e-01
-2.81250030e-02 1.98786378e-01 -2.01001033e-01 -3.84793952e-02
-3.34135890e-02 -6.83034182e-01 -6.32487118e-01 -6.34163797e-01
1.78124994e-01 4.80250657e-01 6.43406630e-01 1.30497778... | [9.678567886352539, 0.8928110599517822] |
c4d21e7d-9bcd-428e-a7ba-2dbd096aacfc | mesograph-automatic-profiling-of-malignant | 2302.12653 | null | https://arxiv.org/abs/2302.12653v1 | https://arxiv.org/pdf/2302.12653v1.pdf | MesoGraph: Automatic Profiling of Malignant Mesothelioma Subtypes from Histological Images | Malignant mesothelioma is classified into three histological subtypes, Epithelioid, Sarcomatoid, and Biphasic according to the relative proportions of epithelioid and sarcomatoid tumor cells present. Biphasic tumors display significant populations of both cell types. This subtyping is subjective and limited by current ... | ['Jan Lukas Robertus', 'Fayyaz Minhas', 'Sanjay Popat', 'Miriam Moffatt', 'William Cookson', 'Danny Jonigk', 'Angeles Montero Fernandez', 'Emmanouil Karteris', 'Judith Offman', 'Xiaohong Gao', 'Silviu Tudor', 'Heba Sailem', 'Mark Eastwood'] | 2023-02-23 | null | null | null | null | ['multiple-instance-learning'] | ['methodology'] | [ 3.02877724e-01 3.60390127e-01 -5.91295004e-01 1.15339831e-01
-9.99726772e-01 -4.82545942e-01 6.58914983e-01 7.24315345e-01
-4.21666473e-01 9.62975502e-01 -4.84556705e-03 -4.74415123e-01
-3.25184256e-01 -7.44792223e-01 -2.11624637e-01 -1.17909658e+00
1.64125767e-02 1.01354849e+00 6.86395466e-02 -1.74777657... | [15.178889274597168, -2.9844260215759277] |
4a0f7152-f59d-456f-8491-4717354482c7 | self-organizing-maps-for-the-visual-analysis | null | null | https://aclanthology.org/W15-1823 | https://aclanthology.org/W15-1823.pdf | Self Organizing Maps for the Visual Analysis of Pitch Contours | null | ['Miriam Butt', 'Dominik Sacha', 'Daniel Keim', 'Christian Rohrdantz', 'Yuki Asano', 'Felix Hamborg', 'Bettina Braun'] | 2015-05-01 | null | null | null | ws-2015-5 | ['art-analysis'] | ['computer-vision'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.321916580200195, 3.9031291007995605] |
8a8a9f12-f943-4e9a-923c-71b85bb51e94 | faster-training-of-diffusion-models-and | 2306.02658 | null | https://arxiv.org/abs/2306.02658v1 | https://arxiv.org/pdf/2306.02658v1.pdf | Faster Training of Diffusion Models and Improved Density Estimation via Parallel Score Matching | In Diffusion Probabilistic Models (DPMs), the task of modeling the score evolution via a single time-dependent neural network necessitates extended training periods and may potentially impede modeling flexibility and capacity. To counteract these challenges, we propose leveraging the independence of learning tasks at d... | ['Marco Lorenzi', 'Etrit Haxholli'] | 2023-06-05 | null | null | null | null | ['density-estimation'] | ['methodology'] | [ 4.46704961e-02 5.43905869e-02 -2.23950058e-01 -2.53488421e-01
-5.50724685e-01 -4.97926414e-01 7.85687983e-01 3.46979797e-01
-5.39263725e-01 7.46904671e-01 -5.06693162e-02 -4.59898859e-01
-3.51022810e-01 -8.36081445e-01 -6.59256577e-01 -6.88642323e-01
-3.70243400e-01 6.42288148e-01 4.36333865e-01 4.90031660... | [7.205053329467773, 3.818230152130127] |
370219cc-37ac-480b-b54c-6775fe1b6cbc | unfolding-local-growth-rate-estimates-for | 2212.06776 | null | https://arxiv.org/abs/2212.06776v1 | https://arxiv.org/pdf/2212.06776v1.pdf | Unfolding Local Growth Rate Estimates for (Almost) Perfect Adversarial Detection | Convolutional neural networks (CNN) define the state-of-the-art solution on many perceptual tasks. However, current CNN approaches largely remain vulnerable against adversarial perturbations of the input that have been crafted specifically to fool the system while being quasi-imperceptible to the human eye. In recent y... | ['Janis Keuper', 'Margret Keuper', 'Peter Lorenz'] | 2022-12-13 | null | null | null | null | ['adversarial-defense', 'adversarial-attack-detection', 'adversarial-attack-detection'] | ['adversarial', 'computer-vision', 'knowledge-base'] | [ 3.30480754e-01 3.49240869e-01 1.25991061e-01 -1.07436135e-01
-6.10475183e-01 -1.15232742e+00 9.28059399e-01 -2.88592209e-03
-5.05865276e-01 3.67047399e-01 4.63135801e-02 -5.11036038e-01
1.02734201e-01 -5.54180145e-01 -1.03770232e+00 -6.27380431e-01
4.39166613e-02 -2.96018898e-01 3.47998172e-01 -2.89545327... | [5.623636245727539, 7.861093997955322] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.