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83ac3e2b-f42c-4b09-b801-2a371c4fb6fb | an-evaluation-of-binary-comparative-lexical | null | null | https://aclanthology.org/2022.bea-1.24 | https://aclanthology.org/2022.bea-1.24.pdf | An Evaluation of Binary Comparative Lexical Complexity Models | Identifying complex words in texts is an important first step in text simplification (TS) systems. In this paper, we investigate the performance of binary comparative Lexical Complexity Prediction (LCP) models applied to a popular benchmark dataset — the CompLex 2.0 dataset used in SemEval-2021 Task 1. With the data fr... | ['Matthew Shardlow', 'Marcos Zampieri', 'Kai North'] | null | null | null | null | naacl-bea-2022-7 | ['lexical-complexity-prediction'] | ['natural-language-processing'] | [ 3.91364157e-01 2.03254186e-02 -3.33798707e-01 -2.52252340e-01
-6.38529181e-01 -3.84951413e-01 6.80979848e-01 6.07819557e-01
-7.47562110e-01 9.87348258e-01 2.76082903e-01 -4.10690963e-01
1.37392640e-01 -4.31823134e-01 -4.51991558e-01 -2.49398574e-01
4.85492706e-01 5.90315342e-01 6.27077371e-02 -5.36258221... | [10.945839881896973, 10.402236938476562] |
9058a331-105d-4293-9238-476fe5661097 | bias-eliminated-semantic-refinement-for-any | 2202.04827 | null | https://arxiv.org/abs/2202.04827v1 | https://arxiv.org/pdf/2202.04827v1.pdf | Bias-Eliminated Semantic Refinement for Any-Shot Learning | When training samples are scarce, the semantic embedding technique, ie, describing class labels with attributes, provides a condition to generate visual features for unseen objects by transferring the knowledge from seen objects. However, semantic descriptions are usually obtained in an external paradigm, such as manua... | ['Xi Li', 'Chunhui Zhao', 'Liangjun Feng'] | 2022-02-10 | null | null | null | null | ['generalized-zero-shot-learning', 'generalized-zero-shot-learning'] | ['computer-vision', 'methodology'] | [ 4.50044900e-01 3.48546863e-01 -1.49154931e-01 -4.63010907e-01
-9.78089035e-01 -5.05805671e-01 8.21127415e-01 -4.61507738e-02
-1.22647919e-01 6.80019379e-01 7.35966861e-02 2.13780493e-01
-1.78698391e-01 -9.57269430e-01 -8.07969272e-01 -1.07413518e+00
2.78490484e-01 4.07763243e-01 6.60485923e-02 -2.53966570... | [10.03982925415039, 2.484173536300659] |
51fe243b-215e-4fa9-88e5-16ec640d5776 | learning-with-noisily-labeled-class | 2211.10955 | null | https://arxiv.org/abs/2211.10955v1 | https://arxiv.org/pdf/2211.10955v1.pdf | Learning with Noisily-labeled Class-imbalanced Data | Real-world large-scale datasets are both noisily labeled and class-imbalanced. The issues seriously hurt the generalization of trained models. It is hence significant to address the simultaneous incorrect labeling and class-imbalance, i.e., the problem of learning with noisy labels on long-tailed data. Previous works d... | ['Weiran Huang', 'Jun Yao', 'Chun Yuan', 'Manyi Zhang'] | 2022-11-20 | null | null | null | null | ['learning-with-noisy-labels', 'learning-with-noisy-labels'] | ['computer-vision', 'natural-language-processing'] | [ 2.70791799e-01 -4.86023873e-02 -4.18505847e-01 -5.14224648e-01
-7.58516312e-01 -4.09084231e-01 1.38384044e-01 3.44604850e-01
-6.66816160e-02 9.84527588e-01 -1.58069760e-01 -3.44639309e-02
-2.42364213e-01 -9.18579519e-01 -6.51833594e-01 -9.71838176e-01
4.73433137e-01 5.17152905e-01 -7.05879927e-02 8.71679094... | [9.202507019042969, 3.9029624462127686] |
8597ce8a-c5af-406c-9472-2265ef4cfb9a | semantic-segmentation-on-3d-point-clouds-with | 2307.01489 | null | https://arxiv.org/abs/2307.01489v1 | https://arxiv.org/pdf/2307.01489v1.pdf | Semantic Segmentation on 3D Point Clouds with High Density Variations | LiDAR scanning for surveying applications acquire measurements over wide areas and long distances, which produces large-scale 3D point clouds with significant local density variations. While existing 3D semantic segmentation models conduct downsampling and upsampling to build robustness against varying point densities,... | ['Tat-Jun Chin', 'Ian Reid', 'Simon Ratcliffe', 'Luke Haub', 'Ryan Faulkner'] | 2023-07-04 | null | null | null | null | ['3d-semantic-segmentation'] | ['computer-vision'] | [-2.07177904e-02 -3.65091935e-02 -1.87594607e-01 -6.58590972e-01
-6.92278147e-01 -4.09879386e-01 4.99290019e-01 1.93440437e-01
-2.33021945e-01 6.19918406e-01 -1.33024529e-01 -1.04449406e-01
-5.63514307e-02 -1.22089326e+00 -9.84847963e-01 -3.21998686e-01
1.58594772e-02 1.27982044e+00 7.95504630e-01 5.23135588... | [8.026556968688965, -2.904064416885376] |
5aa6cd86-7517-4489-ad8c-f5c2bad284a5 | embedding-strategies-for-specialized-domains | null | null | https://aclanthology.org/P19-2041 | https://aclanthology.org/P19-2041.pdf | Embedding Strategies for Specialized Domains: Application to Clinical Entity Recognition | Using pre-trained word embeddings in conjunction with Deep Learning models has become the {``}de facto{''} approach in Natural Language Processing (NLP). While this usually yields satisfactory results, off-the-shelf word embeddings tend to perform poorly on texts from specialized domains such as clinical reports. Moreo... | ['Pierre Zweigenbaum', 'Olivier Ferret', 'Hicham El Boukkouri', 'Thomas Lavergne'] | 2019-07-01 | null | null | null | acl-2019-7 | ['clinical-concept-extraction'] | ['medical'] | [ 1.12922853e-02 2.61436641e-01 -7.94738308e-02 -1.72000542e-01
-9.32896852e-01 -3.59603584e-01 4.20582026e-01 9.95056212e-01
-1.10976636e+00 6.48074210e-01 5.38899004e-01 -4.85586971e-01
-8.92632678e-02 -6.39790714e-01 -1.54191405e-01 -4.38647360e-01
-5.61785288e-02 7.38880754e-01 1.25250831e-01 -3.52784574... | [8.664032936096191, 8.626298904418945] |
11de3e9e-0d4c-4f87-b5a3-fa7e6cd7b700 | dhge-dual-view-hyper-relational-knowledge | 2207.08562 | null | https://arxiv.org/abs/2207.08562v4 | https://arxiv.org/pdf/2207.08562v4.pdf | DHGE: Dual-View Hyper-Relational Knowledge Graph Embedding for Link Prediction and Entity Typing | In the field of representation learning on knowledge graphs (KGs), a hyper-relational fact consists of a main triple and several auxiliary attribute-value descriptions, which is considered more comprehensive and specific than a triple-based fact. However, currently available hyper-relational KG embedding methods in a s... | ['Kaiyang Wan', 'Tianyu Yao', 'Gengxian Zhou', 'Ling Tan', 'Haihong E', 'Haoran Luo'] | 2022-07-18 | null | null | null | null | ['link-prediction-on-dh-kgs', 'entity-typing-on-dh-kgs', 'entity-typing'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-5.77031612e-01 1.01891553e+00 -8.72788787e-01 -3.61714929e-01
-3.98964316e-01 -3.53967696e-02 3.39326084e-01 5.80818355e-01
-4.03847434e-02 9.01780188e-01 7.38371968e-01 -1.12799719e-01
-6.78145766e-01 -1.49114668e+00 -6.68889165e-01 -4.11273450e-01
-5.27853310e-01 6.88837826e-01 1.08342640e-01 -2.63290673... | [8.70729923248291, 7.949190139770508] |
25420963-b048-40ae-9ce8-e47dfc1c19aa | a-semi-lagrangian-two-level-preconditioned | 1604.02153 | null | http://arxiv.org/abs/1604.02153v2 | http://arxiv.org/pdf/1604.02153v2.pdf | A Semi-Lagrangian two-level preconditioned Newton-Krylov solver for constrained diffeomorphic image registration | We propose an efficient numerical algorithm for the solution of diffeomorphic
image registration problems. We use a variational formulation constrained by a
partial differential equation (PDE), where the constraints are a scalar
transport equation.
We use a pseudospectral discretization in space and second-order accu... | ['George Biros', 'Andreas Mang'] | 2016-04-07 | null | null | null | null | ['constrained-diffeomorphic-image-registration'] | ['computer-vision'] | [-7.71614090e-02 -3.21926206e-01 4.84125674e-01 -3.28559009e-03
-9.57740724e-01 -2.61815101e-01 3.19895715e-01 -9.87019539e-02
-8.36088538e-01 9.87265944e-01 -8.99328664e-02 -3.35461050e-01
-1.10007480e-01 -5.56960642e-01 -3.33439797e-01 -9.13479507e-01
-2.66318917e-01 5.66875160e-01 2.37647280e-01 -3.83733988... | [6.487897872924805, 3.296027183532715] |
4c06c2eb-5242-4624-bbf0-b7ef9ae491d2 | 3d-model-based-zero-shot-pose-estimation | 2305.17934 | null | https://arxiv.org/abs/2305.17934v1 | https://arxiv.org/pdf/2305.17934v1.pdf | 3D Model-based Zero-Shot Pose Estimation Pipeline | Most existing learning-based pose estimation methods are typically developed for non-zero-shot scenarios, where they can only estimate the poses of objects present in the training dataset. This setting restricts their applicability to unseen objects in the training phase. In this paper, we introduce a fully zero-shot p... | ['Zhenyu He', 'Liwei Wu', 'Rui Zhao', 'Tianpeng Bao', 'Mingshan Sun', 'Jianqiu Chen'] | 2023-05-29 | null | null | null | null | ['pose-estimation'] | ['computer-vision'] | [-1.58194602e-01 -6.99993968e-02 -6.29875213e-02 -1.54395133e-01
-1.09260523e+00 -4.74330962e-01 4.74567533e-01 6.53914660e-02
-3.48602533e-01 -6.18077070e-02 -4.66578566e-02 1.75646544e-01
1.72044873e-01 -7.20812559e-01 -8.22576284e-01 -4.05839562e-01
3.71515900e-01 1.08795309e+00 1.09074485e+00 -1.16744138... | [7.562166213989258, -2.62715220451355] |
dde4efab-7112-4bfc-915b-7b05bacd4596 | nearest-neighbor-machine-translation-is-meta | 2305.13034 | null | https://arxiv.org/abs/2305.13034v1 | https://arxiv.org/pdf/2305.13034v1.pdf | Nearest Neighbor Machine Translation is Meta-Optimizer on Output Projection Layer | Nearest Neighbor Machine Translation ($k$NN-MT) has achieved great success on domain adaptation tasks by integrating pre-trained Neural Machine Translation (NMT) models with domain-specific token-level retrieval. However, the reasons underlying its success have not been thoroughly investigated. In this paper, we provid... | ['Rui Wang', 'Lemao Liu', 'Yichao Du', 'Zhirui Zhang', 'Ruize Gao'] | 2023-05-22 | null | null | null | null | ['nmt'] | ['computer-code'] | [ 2.03202084e-01 -1.58157840e-01 -6.44883692e-01 -4.67289835e-01
-1.42376709e+00 -7.55663335e-01 7.77553082e-01 7.46433586e-02
-6.76298857e-01 6.47146344e-01 5.24719000e-01 -7.70473480e-01
-7.66078606e-02 -6.28762782e-01 -8.29029441e-01 -1.48775637e-01
3.38843048e-01 6.92550480e-01 -1.44957334e-01 -5.43210685... | [11.640803337097168, 10.135363578796387] |
88af0b1f-5713-48bf-923c-84d21e5cc5df | multispider-towards-benchmarking-multilingual | 2212.13492 | null | https://arxiv.org/abs/2212.13492v1 | https://arxiv.org/pdf/2212.13492v1.pdf | MultiSpider: Towards Benchmarking Multilingual Text-to-SQL Semantic Parsing | Text-to-SQL semantic parsing is an important NLP task, which greatly facilitates the interaction between users and the database and becomes the key component in many human-computer interaction systems. Much recent progress in text-to-SQL has been driven by large-scale datasets, but most of them are centered on English.... | ['Jian-Guang Lou', 'Dechen Zhan', 'Wanxiang Che', 'Dingzirui Wang', 'Mingyang Pan', 'Yan Gao', 'Longxu Dou'] | 2022-12-27 | null | null | null | null | ['text-to-sql', 'semantic-parsing'] | ['computer-code', 'natural-language-processing'] | [-7.28324950e-02 -4.69965599e-02 -3.88448775e-01 -5.71622312e-01
-1.18195796e+00 -8.15125406e-01 5.41386187e-01 3.95419747e-01
-4.87384886e-01 6.02023244e-01 4.78650957e-01 -5.97144127e-01
2.37884358e-01 -6.40069067e-01 -8.25278223e-01 9.35201421e-02
5.17238796e-01 6.63425744e-01 2.40290478e-01 -5.63205779... | [9.897665023803711, 7.926238059997559] |
e20c5535-14f4-4186-b7bb-7501bac3ab0b | eventgraph-at-case-2021-task-1-a-general | 2210.09770 | null | https://arxiv.org/abs/2210.09770v1 | https://arxiv.org/pdf/2210.09770v1.pdf | EventGraph at CASE 2021 Task 1: A General Graph-based Approach to Protest Event Extraction | This paper presents our submission to the 2022 edition of the CASE 2021 shared task 1, subtask 4. The EventGraph system adapts an end-to-end, graph-based semantic parser to the task of Protest Event Extraction and more specifically subtask 4 on event trigger and argument extraction. We experiment with various graphs, e... | ['Lilja Øvrelid', 'Samia Touileb', 'David Samuel', 'Huiling You'] | 2022-10-18 | null | null | null | null | ['event-extraction'] | ['natural-language-processing'] | [-2.21424535e-01 4.68544364e-01 -3.96310180e-01 -3.49907011e-01
-9.82750893e-01 -9.42861021e-01 9.20821667e-01 4.71683174e-01
-3.52817476e-01 7.39182711e-01 8.44285548e-01 -4.18759555e-01
-1.15441792e-02 -8.05716515e-01 -5.69837987e-01 3.45991626e-02
-3.30124684e-02 5.24768770e-01 6.80553675e-01 -2.24181309... | [9.0250883102417, 9.26109504699707] |
c6b7a836-13d2-4039-be84-f9bd939bda6c | face-frontalization-based-on-robustly-fitting | 2010.13676 | null | https://arxiv.org/abs/2010.13676v2 | https://arxiv.org/pdf/2010.13676v2.pdf | Face Frontalization Based on Robustly Fitting a Deformable Shape Model to 3D Landmarks | Face frontalization consists of synthesizing a frontally-viewed face from an arbitrarily-viewed one. The main contribution of this paper is a robust face alignment method that enables pixel-to-pixel warping. The method simultaneously estimates the rigid transformation (scale, rotation, and translation) and the non-rigi... | ['Radu Horaud', 'Mostafa Sadeghi', 'Zhiqi Kang'] | 2020-10-26 | null | null | null | null | ['robust-face-alignment', 'face-alignment', 'face-model'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 3.20806563e-01 1.79825082e-01 3.13581258e-01 -4.89682585e-01
-8.57565761e-01 -6.07760668e-01 6.17520928e-01 -4.04055744e-01
-3.54512036e-01 4.26411629e-01 -6.63250610e-02 2.22273350e-01
-1.43868430e-03 -4.39419627e-01 -8.77840817e-01 -9.43954051e-01
8.45490694e-02 6.89799845e-01 -1.52947038e-01 1.00002348... | [13.154034614562988, 0.04096107929944992] |
f1ebecae-cd12-4a42-9fb1-a1f3c8e3b123 | discern-discourse-aware-entailment-reasoning | 2010.01838 | null | https://arxiv.org/abs/2010.01838v3 | https://arxiv.org/pdf/2010.01838v3.pdf | Discern: Discourse-Aware Entailment Reasoning Network for Conversational Machine Reading | Document interpretation and dialog understanding are the two major challenges for conversational machine reading. In this work, we propose Discern, a discourse-aware entailment reasoning network to strengthen the connection and enhance the understanding for both document and dialog. Specifically, we split the document ... | ['Michael R. Lyu', 'Irwin King', 'Caiming Xiong', 'Steven C. H. Hoi', 'Shafiq Joty', 'Jingjing Li', 'Chien-Sheng Wu', 'Yifan Gao'] | 2020-10-05 | null | https://aclanthology.org/2020.emnlp-main.191 | https://aclanthology.org/2020.emnlp-main.191.pdf | emnlp-2020-11 | ['discourse-segmentation'] | ['natural-language-processing'] | [ 4.80752319e-01 8.64788949e-01 -2.12699190e-01 -6.75325274e-01
-1.22152793e+00 -8.33776176e-01 9.17229950e-01 1.61176428e-01
-2.04949021e-01 8.07157576e-01 8.98012638e-01 -9.42714751e-01
2.38717273e-01 -5.69063306e-01 -4.84972835e-01 -2.01989502e-01
5.16535997e-01 7.81602919e-01 5.88672869e-02 -4.94685292... | [11.91686725616455, 8.061984062194824] |
76ba1880-1be3-45ae-81d4-000ea6382d98 | map-disparity-estimation-using-hidden-markov | null | null | http://openaccess.thecvf.com/content_iccv_2015/html/Psota_MAP_Disparity_Estimation_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Psota_MAP_Disparity_Estimation_ICCV_2015_paper.pdf | MAP Disparity Estimation Using Hidden Markov Trees | A new method is introduced for stereo matching that operates on minimum spanning trees (MSTs) generated from the images. Disparity maps are represented as a collection of hidden states on MSTs, and each MST is modeled as a hidden Markov tree. An efficient recursive message-passing scheme designed to operate on hidden M... | ['Mateusz Mittek', 'Jedrzej Kowalczuk', 'Eric T. Psota', 'Lance C. Perez'] | 2015-12-01 | null | null | null | iccv-2015-12 | ['stereo-matching'] | ['computer-vision'] | [ 7.53594637e-01 -7.49209290e-03 -3.20927799e-01 -7.10131288e-01
-9.42304015e-01 1.02099352e-01 6.50862336e-01 1.56060755e-01
-3.58778596e-01 5.63085437e-01 4.28445451e-02 -3.12633306e-01
2.83035427e-01 -9.83689368e-01 -6.81430876e-01 -7.49810457e-01
-1.62988991e-01 2.45054692e-01 7.59491622e-01 3.05160195... | [9.068675994873047, -2.5123705863952637] |
ce4d9056-4fe4-4e0f-aa3f-7c6d747735ea | neural-structure-mapping-for-learning-1 | null | null | https://openreview.net/forum?id=l-TLGjxwajn | https://openreview.net/pdf?id=l-TLGjxwajn | Neural Structure Mapping For Learning Abstract Visual Analogies | Building conceptual abstractions from sensory information and then reasoning about them is central to human intelligence. Abstract reasoning both relies on, and is facilitated by, our ability to make analogies about concepts from known domains to novel domains. Structure Mapping Theory of human analogical reasoning pos... | ['Graham W. Taylor', 'Shashank Shekhar'] | 2021-10-12 | neural-structure-mapping-for-learning | null | null | neurips-workshop-svrhm-2021-12 | ['visual-analogies'] | ['computer-vision'] | [ 3.46585989e-01 2.97885269e-01 1.32324800e-01 -4.43702698e-01
-7.73860700e-03 -7.25214124e-01 9.39775467e-01 5.29124558e-01
-3.03719610e-01 5.68830609e-01 3.51440102e-01 -7.02445745e-01
-3.80051821e-01 -9.91274893e-01 -7.83375025e-01 1.53986454e-01
1.12380907e-01 9.37383115e-01 3.10240030e-01 -4.96233344... | [10.595159530639648, 2.309239387512207] |
0925c820-ce2c-420e-9c43-b1abc0dda90f | sparse-local-patch-transformer-for-robust | 2203.06541 | null | https://arxiv.org/abs/2203.06541v2 | https://arxiv.org/pdf/2203.06541v2.pdf | Sparse Local Patch Transformer for Robust Face Alignment and Landmarks Inherent Relation Learning | Heatmap regression methods have dominated face alignment area in recent years while they ignore the inherent relation between different landmarks. In this paper, we propose a Sparse Local Patch Transformer (SLPT) for learning the inherent relation. The SLPT generates the representation of each single landmark from a lo... | ['Min Xu', 'Xi Wang', 'JianGuo Zhang', 'Wenjian Huang', 'Weiwei qu', 'Jiahao Xia'] | 2022-03-13 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Xia_Sparse_Local_Patch_Transformer_for_Robust_Face_Alignment_and_Landmarks_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Xia_Sparse_Local_Patch_Transformer_for_Robust_Face_Alignment_and_Landmarks_CVPR_2022_paper.pdf | cvpr-2022-1 | ['robust-face-alignment', 'face-alignment'] | ['computer-vision', 'computer-vision'] | [-1.51688486e-01 6.65754378e-02 -2.79934943e-01 -5.80809057e-01
-7.71447778e-01 1.58396345e-02 6.38471365e-01 -3.35168064e-01
4.49010096e-02 4.85615194e-01 2.13264272e-01 4.79872108e-01
-9.17835012e-02 -7.11543918e-01 -7.47524619e-01 -7.52436996e-01
7.79903308e-02 3.28475118e-01 1.83912709e-01 -2.55629141... | [13.498050689697266, 0.434471994638443] |
75b69114-d1f5-47b6-b7ab-fc6cc61b425d | spike-and-slab-generalized-additive-models | 2110.14449 | null | https://arxiv.org/abs/2110.14449v3 | https://arxiv.org/pdf/2110.14449v3.pdf | Spike-and-Slab LASSO Generalized Additive Models and Scalable Algorithms for High-Dimensional Data Analysis | There are proposals that extend the classical generalized additive models (GAMs) to accommodate high-dimensional data ($p>>n$) using group sparse regularization. However, the sparse regularization may induce excess shrinkage when estimating smooth functions, damaging predictive performance. Moreover, most of these GAMs... | ['Nengjun Yi', 'D. Leann Long', 'A. K. M. Fazlur Rahman', 'Byron C. Jaeger', 'Boyi Guo'] | 2021-10-27 | null | null | null | null | ['additive-models'] | ['methodology'] | [ 8.06249753e-02 -2.31157079e-01 -2.01242894e-01 -7.17918038e-01
-8.04335475e-01 -3.33782792e-01 4.45362031e-02 1.01844184e-01
-1.17470987e-01 7.57110238e-01 2.14638516e-01 -1.99224770e-01
-3.45460862e-01 -6.08010769e-01 -7.15649128e-01 -9.46585834e-01
-1.12872377e-01 2.00553730e-01 -3.28895748e-01 1.46017075... | [7.417416572570801, 4.61798620223999] |
8e678dcb-0f8e-4b77-9639-5c5b9fe051e1 | autoregressive-latent-video-prediction-with | null | null | https://openreview.net/forum?id=K-hiHQXEQog | https://openreview.net/pdf?id=K-hiHQXEQog | Autoregressive Latent Video Prediction with High-Fidelity Image Generator | Video prediction is an important yet challenging problem; burdened with the tasks of generating future frames and learning environment dynamics. Recently, autoregressive latent video models have proved to be a powerful video prediction tool, by separating the video prediction into two sub-problems: pre-training an imag... | ['Pieter Abbeel', 'Stephen James', 'Fangchen Liu', 'Kimin Lee', 'Younggyo Seo'] | 2021-09-29 | null | null | null | null | ['video-prediction'] | ['computer-vision'] | [ 2.65500337e-01 5.28801791e-02 -2.86285639e-01 -2.83386946e-01
-9.29962754e-01 -1.41205817e-01 6.46896958e-01 -7.03431487e-01
1.31623492e-01 7.87514925e-01 5.96813500e-01 -6.83595240e-02
3.22309047e-01 -6.92040205e-01 -1.13903618e+00 -7.66353011e-01
-1.14095159e-01 2.32835367e-01 1.62652448e-01 8.92226398... | [10.703797340393066, -0.6697838306427002] |
9926b13e-94e5-4c9e-b6ba-f34798b00ca3 | dynamic-enhancement-network-for-partial-multi | 2305.15762 | null | https://arxiv.org/abs/2305.15762v1 | https://arxiv.org/pdf/2305.15762v1.pdf | Dynamic Enhancement Network for Partial Multi-modality Person Re-identification | Many existing multi-modality studies are based on the assumption of modality integrity. However, the problem of missing arbitrary modalities is very common in real life, and this problem is less studied, but actually important in the task of multi-modality person re-identification (Re-ID). To this end, we design a nove... | ['Jin Tang', 'Chenglong Li', 'Zi Wang', 'Ziling He', 'Aihua Zheng'] | 2023-05-25 | null | null | null | null | ['person-re-identification'] | ['computer-vision'] | [ 1.76890835e-01 -7.06802547e-01 6.60814345e-02 -3.44744861e-01
-2.55308867e-01 -4.10972029e-01 5.76146543e-01 -4.20171469e-01
-5.67031384e-01 5.17030656e-01 3.17303121e-01 3.33449543e-01
-1.03471421e-01 -7.57199049e-01 -3.76950324e-01 -9.52797234e-01
4.39902514e-01 1.81656569e-01 -1.61720321e-01 -4.23295826... | [14.644067764282227, 0.9632728695869446] |
f9f21f73-3046-477d-830d-6624285bad55 | adaptive-fine-grained-sketch-based-image | 2207.01723 | null | https://arxiv.org/abs/2207.01723v3 | https://arxiv.org/pdf/2207.01723v3.pdf | Adaptive Fine-Grained Sketch-Based Image Retrieval | The recent focus on Fine-Grained Sketch-Based Image Retrieval (FG-SBIR) has shifted towards generalising a model to new categories without any training data from them. In real-world applications, however, a trained FG-SBIR model is often applied to both new categories and different human sketchers, i.e., different draw... | ['Yi-Zhe Song', 'Tao Xiang', 'Pinaki Nath Chowdhury', 'Animesh Gupta', 'Parth Shah', 'Aneeshan Sain', 'Ayan Kumar Bhunia'] | 2022-07-04 | null | null | null | null | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 3.83933932e-01 -3.54092836e-01 -3.36416066e-01 -3.40354174e-01
-8.39655876e-01 -6.79624736e-01 9.18066800e-01 -2.15538993e-01
-2.85152704e-01 4.65369940e-01 1.20346181e-01 1.21260732e-01
-1.14944860e-01 -7.58845448e-01 -6.53881133e-01 -5.41587472e-01
2.71746993e-01 5.27440310e-01 3.78999472e-01 -3.76657218... | [11.589080810546875, 0.6728093028068542] |
45e993a1-5e38-4af3-ad09-698e048258ac | marginmatch-improving-semi-supervised | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Sosea_MarginMatch_Improving_Semi-Supervised_Learning_with_Pseudo-Margins_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Sosea_MarginMatch_Improving_Semi-Supervised_Learning_with_Pseudo-Margins_CVPR_2023_paper.pdf | MarginMatch: Improving Semi-Supervised Learning with Pseudo-Margins | We introduce MarginMatch, a new SSL approach combining consistency regularization and pseudo-labeling, with its main novelty arising from the use of unlabeled data training dynamics to measure pseudo-label quality. Instead of using only the model's confidence on an unlabeled example at an arbitrary iteration to dec... | ['Cornelia Caragea', 'Tiberiu Sosea'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['pseudo-label'] | ['miscellaneous'] | [ 4.24869508e-02 2.76960939e-01 -4.18081403e-01 -7.27747202e-01
-1.06325495e+00 -4.90089089e-01 7.73249745e-01 7.45927989e-02
-6.03079259e-01 8.53174090e-01 -2.69669026e-01 -2.28319820e-02
1.46096110e-01 -6.22964129e-02 -9.07633126e-01 -7.82199562e-01
1.18464187e-01 6.64366782e-01 3.66494536e-01 3.18681657... | [9.454885482788086, 3.6236863136291504] |
e7e41f12-a110-4bd9-bb73-a6ac592dc76f | are-we-evaluating-paraphrase-generation | null | null | https://openreview.net/forum?id=YBujxHx3VmL | https://openreview.net/pdf?id=YBujxHx3VmL | Are We Evaluating Paraphrase Generation Accurately? | Paraphrase is a restatement of a text that conveys the same meaning using different expressions. The evaluation of paraphrase generation (PG) is a complex task and currently lacks a complete picture of the criteria and metrics. In this paper, we survey the automatic evaluation metrics and human evaluation criteria of P... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['paraphrase-generation', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing'] | [ 4.89463881e-02 2.08153925e-03 -1.11832291e-01 -4.73155707e-01
-8.61118555e-01 -8.65835488e-01 7.91872144e-01 1.93141446e-01
-1.96514532e-01 7.93743372e-01 8.43366563e-01 -1.08488873e-01
-1.29990384e-01 -5.34086049e-01 -8.48956332e-02 -2.32692599e-01
8.86301517e-01 4.55078959e-01 -3.44034731e-02 -6.01855934... | [11.513242721557617, 9.132925987243652] |
66c86086-879e-4abc-93ab-46f4745d5919 | 190807844 | 1908.07844 | null | https://arxiv.org/abs/1908.07844v1 | https://arxiv.org/pdf/1908.07844v1.pdf | Similarity Learning for Authorship Verification in Social Media | Authorship verification tries to answer the question if two documents with unknown authors were written by the same author or not. A range of successful technical approaches has been proposed for this task, many of which are based on traditional linguistic features such as n-grams. These algorithms achieve good results... | ['Dorothea Kolossa', 'Steffen Zeiler', 'Robert M. Nickel', 'Benedikt Boenninghoff'] | 2019-08-20 | null | null | null | null | ['authorship-verification'] | ['natural-language-processing'] | [-2.17764169e-01 -5.00617862e-01 -2.07550168e-01 -3.56244117e-01
-4.91471946e-01 -6.52480245e-01 8.89860868e-01 4.98031706e-01
-2.79388994e-01 6.32373810e-01 1.84496436e-02 -2.61222422e-01
1.63698979e-02 -6.31623328e-01 -1.74431428e-01 -4.54491109e-01
2.60661721e-01 5.39160728e-01 2.90460497e-01 -1.22260973... | [9.557761192321777, 10.595133781433105] |
935a3ee6-3135-4aa2-819f-3ebebf8fc3c9 | efficient-semantic-segmentation-on-edge | 2212.13691 | null | https://arxiv.org/abs/2212.13691v2 | https://arxiv.org/pdf/2212.13691v2.pdf | Efficient Semantic Segmentation on Edge Devices | Semantic segmentation works on the computer vision algorithm for assigning each pixel of an image into a class. The task of semantic segmentation should be performed with both accuracy and efficiency. Most of the existing deep FCNs yield to heavy computations and these networks are very power hungry, unsuitable for rea... | ['Maryam Rahnemoonfar', 'Ting Zhu', 'Guanqun Song', 'Venkatesh Dasari', 'Irfan Ali', 'Farshad Safavi'] | 2022-12-28 | null | null | null | null | ['real-time-semantic-segmentation'] | ['computer-vision'] | [ 2.49538407e-01 1.84324175e-01 3.60702276e-01 -5.77473462e-01
-8.49263147e-02 -8.83406878e-01 4.38617289e-01 -1.18308648e-01
-6.52902901e-01 6.61141396e-01 -2.41764992e-01 -6.08179629e-01
-2.14715973e-01 -1.42735314e+00 -4.84026283e-01 -5.81824481e-01
-3.50593716e-01 1.03113842e+00 5.87377906e-01 -3.47209126... | [9.2211332321167, -1.328510046005249] |
6cbf1698-c7fc-4395-92a6-12c28f2e7bd2 | noisy-parallel-approximate-decoding-for | 1605.03835 | null | http://arxiv.org/abs/1605.03835v1 | http://arxiv.org/pdf/1605.03835v1.pdf | Noisy Parallel Approximate Decoding for Conditional Recurrent Language Model | Recent advances in conditional recurrent language modelling have mainly
focused on network architectures (e.g., attention mechanism), learning
algorithms (e.g., scheduled sampling and sequence-level training) and novel
applications (e.g., image/video description generation, speech recognition,
etc.) On the other hand, ... | ['Kyunghyun Cho'] | 2016-05-12 | null | null | null | null | ['video-description'] | ['computer-vision'] | [ 5.92052460e-01 1.42743543e-01 -4.00631040e-01 -1.30923226e-01
-8.40838075e-01 -1.84928313e-01 7.54699945e-01 -2.46876091e-01
-3.79682511e-01 8.26940119e-01 2.59488314e-01 -7.17741191e-01
3.08426768e-01 -4.89437759e-01 -8.71072948e-01 -8.48387182e-01
2.58894920e-01 4.87915695e-01 -1.67463213e-01 -1.46365851... | [10.983474731445312, 6.738154888153076] |
d70bbab7-2562-4122-8167-85f13270a802 | finding-a-needle-in-the-haystack-attention | 1811.08513 | null | https://arxiv.org/abs/1811.08513v2 | https://arxiv.org/pdf/1811.08513v2.pdf | Attention-Based Deep Neural Networks for Detection of Cancerous and Precancerous Esophagus Tissue on Histopathological Slides | Deep learning-based methods, such as the sliding window approach for cropped-image classification and heuristic aggregation for whole-slide inference, for analyzing histological patterns in high-resolution microscopy images have shown promising results. These approaches, however, require a laborious annotation process ... | ['Saeed Hassanpour', 'Bing Ren', 'Arief Suriawinata', 'Naofumi Tomita', 'Jason Wei', 'Behnaz Abdollahi'] | 2018-11-20 | null | null | null | null | ['medical-object-detection', 'crop-classification'] | ['computer-vision', 'miscellaneous'] | [ 2.42706522e-01 9.39764604e-02 -3.14642876e-01 -2.85892412e-02
-1.55660856e+00 -7.21505582e-01 1.52745232e-01 6.08535171e-01
-8.71222019e-01 6.70260191e-01 3.97729538e-02 -6.13124371e-01
-2.38062620e-01 -6.56220078e-01 -7.42443025e-01 -1.34132886e+00
-2.54024923e-01 2.23162845e-01 1.25443205e-01 2.57857680... | [15.090215682983398, -2.9988787174224854] |
9f30093c-2d56-44ff-9222-f7c6c9b9d99f | siamese-sleep-transformer-for-robust-sleep | 2212.13919 | null | https://arxiv.org/abs/2212.13919v1 | https://arxiv.org/pdf/2212.13919v1.pdf | Siamese Sleep Transformer For Robust Sleep Stage Scoring With Self-knowledge Distillation and Selective Batch Sampling | In this paper, we propose a Siamese sleep transformer (SST) that effectively extracts features from single-channel raw electroencephalogram signals for robust sleep stage scoring. Despite the significant advances in sleep stage scoring in the last few years, most of them mainly focused on the increment of model perform... | ['Gi-Hwan Shin', 'Young-Seok Kweon', 'Heon-Gyu Kwak'] | 2022-12-12 | null | null | null | null | ['self-knowledge-distillation'] | ['computer-vision'] | [ 5.46625182e-02 -1.37673676e-01 -6.80089667e-02 -5.80051482e-01
-4.41700906e-01 -1.76262006e-01 3.41671228e-01 -9.43936259e-02
-7.12115765e-01 1.00538135e+00 -7.28334114e-02 2.80171007e-01
-3.26987922e-01 -3.40385497e-01 -3.09217960e-01 -8.35993826e-01
-1.26329511e-01 2.35753909e-01 4.44825023e-01 -2.15287022... | [13.5126371383667, 3.527578592300415] |
262dcea8-45a6-474f-80ef-70e72bbc2aa8 | deepatom-a-framework-for-protein-ligand | 1912.00318 | null | https://arxiv.org/abs/1912.00318v1 | https://arxiv.org/pdf/1912.00318v1.pdf | DeepAtom: A Framework for Protein-Ligand Binding Affinity Prediction | The cornerstone of computational drug design is the calculation of binding affinity between two biological counterparts, especially a chemical compound, i.e., a ligand, and a protein. Predicting the strength of protein-ligand binding with reasonable accuracy is critical for drug discovery. In this paper, we propose a d... | ['Dapeng Wu', 'Xiaolin Li', 'Mohammad A. Rezaei', 'Yanjun Li', 'Chenglong Li'] | 2019-12-01 | null | null | null | null | ['molecular-docking'] | ['medical'] | [ 5.88977057e-03 -3.20844173e-01 -3.38316828e-01 -3.33957404e-01
-8.02712023e-01 -3.74121368e-01 1.65789664e-01 3.80218536e-01
-5.18977106e-01 1.31561518e+00 -2.02802643e-02 -4.37523812e-01
-7.13354647e-02 -6.56606555e-01 -1.02689302e+00 -8.99510860e-01
-9.84545052e-02 7.20916271e-01 1.15528695e-01 -2.61124730... | [4.907860279083252, 5.65952205657959] |
f2271078-fab2-4158-aac6-463610f47972 | convolution-free-waveform-transformers-for | 2109.15129 | null | https://arxiv.org/abs/2109.15129v1 | https://arxiv.org/pdf/2109.15129v1.pdf | Convolution-Free Waveform Transformers for Multi-Lead ECG Classification | We present our entry to the 2021 PhysioNet/CinC challenge - a waveform transformer model to detect cardiac abnormalities from ECG recordings. We compare the performance of the waveform transformer model on different ECG-lead subsets using approximately 88,000 ECG recordings from six datasets. In the official rankings, ... | ['Jonathan Rubin', 'Corneliu Antonescu', 'Yale Chang', 'Gregory Boverman', 'Annamalai Natarajan'] | 2021-09-29 | null | null | null | null | ['ecg-classification'] | ['medical'] | [ 1.76158831e-01 -2.54352391e-01 7.59927481e-02 -2.76868284e-01
-1.66995168e+00 -1.07650602e+00 -2.91947693e-01 3.09796304e-01
-8.94917548e-02 8.97186041e-01 3.12663913e-01 -3.32286745e-01
-8.62384677e-01 -6.46342710e-02 -2.54926473e-01 -1.59954578e-01
-9.67826724e-01 5.81325889e-01 9.54899415e-02 6.70097843... | [14.36038875579834, 3.2723824977874756] |
192f2047-775e-476b-812c-31a3f5d0aee7 | assembled-openml-creating-efficient | 2307.00285 | null | https://arxiv.org/abs/2307.00285v1 | https://arxiv.org/pdf/2307.00285v1.pdf | Assembled-OpenML: Creating Efficient Benchmarks for Ensembles in AutoML with OpenML | Automated Machine Learning (AutoML) frameworks regularly use ensembles. Developers need to compare different ensemble techniques to select appropriate techniques for an AutoML framework from the many potential techniques. So far, the comparison of ensemble techniques is often computationally expensive, because many bas... | ['Joeran Beel', 'Lennart Purucker'] | 2023-07-01 | null | null | null | null | ['automl'] | ['methodology'] | [-8.02801400e-02 -1.24451388e-02 4.84623134e-01 -8.41714382e-01
-1.12009108e+00 -5.92703581e-01 4.58246619e-01 3.30434054e-01
-1.72397941e-01 1.04545450e+00 -4.70986515e-01 -4.66994673e-01
-1.61874101e-01 -7.62335002e-01 -8.14919531e-01 -6.03493214e-01
-9.21574309e-02 7.32242346e-01 -1.20912097e-01 -3.24198194... | [8.762022018432617, 6.9893341064453125] |
20e8925b-9dfd-4834-8e70-efaa2e762cc1 | multi-task-transformer-with-relation | 2303.10870 | null | https://arxiv.org/abs/2303.10870v1 | https://arxiv.org/pdf/2303.10870v1.pdf | Multi-task Transformer with Relation-attention and Type-attention for Named Entity Recognition | Named entity recognition (NER) is an important research problem in natural language processing. There are three types of NER tasks, including flat, nested and discontinuous entity recognition. Most previous sequential labeling models are task-specific, while recent years have witnessed the rising of generative models d... | ['Zhoujun Li', 'Wei Wu', 'Jingang Wang', 'Zenglin Xu', 'Qifan Wang', 'Jiahao Liu', 'Hongyin Tang', 'Ying Mo'] | 2023-03-20 | null | null | null | null | ['boundary-detection'] | ['computer-vision'] | [ 3.61308712e-03 4.67531830e-02 5.17068431e-02 -6.71079636e-01
-1.09627759e+00 -6.56373501e-01 4.60236490e-01 -5.12305833e-02
-7.39448965e-01 7.68747389e-01 6.14052236e-01 -2.16528922e-01
4.75110650e-01 -8.85247052e-01 -5.74297547e-01 -4.63242322e-01
1.69385120e-01 5.13033867e-01 4.15268540e-02 -7.28124976... | [9.689179420471191, 9.507554054260254] |
ada43423-7c24-40a9-9e9c-18fb72a211fd | on-the-limitations-of-unsupervised-bilingual | 1805.03620 | null | http://arxiv.org/abs/1805.03620v1 | http://arxiv.org/pdf/1805.03620v1.pdf | On the Limitations of Unsupervised Bilingual Dictionary Induction | Unsupervised machine translation---i.e., not assuming any cross-lingual
supervision signal, whether a dictionary, translations, or comparable
corpora---seems impossible, but nevertheless, Lample et al. (2018) recently
proposed a fully unsupervised machine translation (MT) model. The model relies
heavily on an adversari... | ['Ivan Vulić', 'Anders Søgaard', 'Sebastian Ruder'] | 2018-05-09 | on-the-limitations-of-unsupervised-bilingual-1 | https://aclanthology.org/P18-1072 | https://aclanthology.org/P18-1072.pdf | acl-2018-7 | ['graph-similarity', 'unsupervised-machine-translation'] | ['graphs', 'natural-language-processing'] | [ 2.82593906e-01 1.26496106e-01 -6.74773812e-01 -5.70277944e-02
-7.02055514e-01 -1.09836292e+00 9.43984926e-01 2.29452193e-01
-6.01694226e-01 8.21775198e-01 3.68148088e-01 -7.37965047e-01
2.12498903e-01 -6.71243608e-01 -7.34240055e-01 -6.21424496e-01
3.48756239e-02 9.35966492e-01 -2.43690893e-01 -4.45047855... | [11.074645042419434, 10.08076286315918] |
6c3f33a1-b588-4913-8668-c515adefbce3 | coderetriever-unimodal-and-bimodal-1 | 2201.10866 | null | https://arxiv.org/abs/2201.10866v3 | https://arxiv.org/pdf/2201.10866v3.pdf | CodeRetriever: Unimodal and Bimodal Contrastive Learning for Code Search | In this paper, we propose the CodeRetriever model, which learns the function-level code semantic representations through large-scale code-text contrastive pre-training. We adopt two contrastive learning schemes in CodeRetriever: unimodal contrastive learning and bimodal contrastive learning. For unimodal contrastive le... | ['Nan Duan', 'Weizhu Chen', 'Daxin Jiang', 'Weizhen Qi', 'Bolun Yao', 'Hang Zhang', 'Xipeng Qiu', 'Yelong Shen', 'Yeyun Gong', 'Xiaonan Li'] | 2022-01-26 | null | null | null | null | ['code-search', 'code-search'] | ['computer-code', 'computer-vision'] | [-1.25113860e-01 -3.13439131e-01 -6.68943405e-01 -4.96445954e-01
-1.07417846e+00 -7.33507931e-01 5.91647863e-01 3.71414781e-01
4.72430931e-03 -2.44544689e-02 3.57515514e-01 -8.06277514e-01
9.27232504e-02 -3.89264226e-01 -6.90501451e-01 -1.91122163e-02
-6.03556037e-02 1.02608688e-01 2.53670412e-04 -3.36317122... | [7.546515941619873, 8.038717269897461] |
581f665b-7bb5-4ca4-ad7c-2a26adc25b20 | resnet18-model-with-sequential-layer-for | null | null | https://ijcrt.org/papers/IJCRT2205235.pdf | https://ijcrt.org/papers/IJCRT2205235.pdf | Resnet18 Model With Sequential Layer For Computing Accuracy On Image Classification Dataset | This residual network has been a broad domain of research in deep learning. Many complex architectures are based upon
residual networks. Residual networks are efficient due to skip connections. This paper highlights the addition of a sequential layer
to the traditional RESNET 18 model for computing the accuracy of an... | ['Dr. Laxman Sahoo', 'Dr. Venkateswara Rao Gurrala', 'Allena Venkata Sai Abhishek'] | 2022-05-01 | null | null | null | ijcrt-2022-5 | ['image-manipulation-detection', 'image-smoothing', 'image-matting', 'image-variation', 'image-stitching', 'image-augmentation', 'image-manipulation', 'image-morphing', 'roi-based-image-generation', 'image-cropping', 'detecting-image-manipulation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [-2.06984188e-02 -2.85756618e-01 8.65560770e-02 -6.79913044e-01
2.95978397e-01 -2.19453216e-01 3.91190529e-01 -7.76226223e-02
-6.55031979e-01 7.63061523e-01 1.46625340e-01 -2.10860178e-01
-7.85926133e-02 -1.00694311e+00 -4.53754127e-01 -5.35019219e-01
6.94944933e-02 6.28218800e-02 5.01886845e-01 -5.31370491... | [9.269281387329102, 2.198275566101074] |
f21d00fb-859d-4663-95bc-17eaaf382174 | revisiting-shallow-discourse-parsing-in-the-1 | 2204.00350 | null | https://arxiv.org/abs/2204.00350v1 | https://arxiv.org/pdf/2204.00350v1.pdf | Revisiting Shallow Discourse Parsing in the PDTB-3: Handling Intra-sentential Implicits | In the PDTB-3, several thousand implicit discourse relations were newly annotated \textit{within} individual sentences, adding to the over 15,000 implicit relations annotated \textit{across} adjacent sentences in the PDTB-2. Given that the position of the arguments to these \textit{intra-sentential implicits} is no lon... | ['Bonnie Webber', 'Zheng Zhao'] | 2022-04-01 | revisiting-shallow-discourse-parsing-in-the | https://aclanthology.org/2021.codi-main.10 | https://aclanthology.org/2021.codi-main.10.pdf | codi-2021-11 | ['discourse-parsing', 'implicit-relations'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.83824372e-01 1.35529518e+00 -4.33360845e-01 -3.96182746e-01
-4.62531507e-01 -1.02706587e+00 9.62470055e-01 7.24812329e-01
-3.55408102e-01 1.31866562e+00 9.03416216e-01 -8.41655850e-01
-4.78541136e-01 -4.65839922e-01 -3.95318717e-01 -2.23887235e-01
-1.06320314e-01 6.89249158e-01 7.02788472e-01 -5.10498106... | [10.755929946899414, 9.407858848571777] |
41732d18-2822-42bd-9b31-9c4cb8241337 | a-graph-based-approach-for-mitigating-multi | 2107.03415 | null | https://arxiv.org/abs/2107.03415v1 | https://arxiv.org/pdf/2107.03415v1.pdf | A Graph-based Approach for Mitigating Multi-sided Exposure Bias in Recommender Systems | Fairness is a critical system-level objective in recommender systems that has been the subject of extensive recent research. A specific form of fairness is supplier exposure fairness where the objective is to ensure equitable coverage of items across all suppliers in recommendations provided to users. This is especiall... | ['Robin Burke', 'Bamshad Mobasher', 'Mykola Pechenizkiy', 'Himan Abdollahpouri', 'Masoud Mansoury'] | 2021-07-07 | null | null | null | null | ['exposure-fairness'] | ['adversarial'] | [-1.50161967e-01 1.77063397e-03 -4.99001265e-01 -4.80900854e-01
-3.24815243e-01 -7.61879504e-01 2.16810301e-01 6.28603280e-01
-2.43982658e-01 5.97926259e-01 5.65109670e-01 -2.35564768e-01
-6.33046031e-01 -1.03068793e+00 -3.49973261e-01 -2.66233891e-01
-4.78857663e-04 4.18432385e-01 -2.34453250e-02 -6.80780172... | [9.706549644470215, 5.652253150939941] |
d5b99ade-cf47-4864-917a-26e4bd0b67fd | time-aware-test-case-execution-scheduling-for | 1902.04627 | null | http://arxiv.org/abs/1902.04627v1 | http://arxiv.org/pdf/1902.04627v1.pdf | Time-aware Test Case Execution Scheduling for Cyber-Physical Systems | Testing cyber-physical systems involves the execution of test cases on
target-machines equipped with the latest release of a software control system.
When testing industrial robots, it is common that the target machines need to
share some common resources, e.g., costly hardware devices, and so there is a
need to schedu... | ['Morten Mossige', 'Mats Carlsson', 'Hein Meling', 'Helge Spieker', 'Arnaud Gotlieb'] | 2019-02-12 | null | null | null | null | ['industrial-robots'] | ['robots'] | [ 1.99988291e-01 1.74930468e-01 -8.51463452e-02 -2.56033152e-01
-1.50934905e-01 -8.30900848e-01 -5.79349287e-02 -5.19451648e-02
1.71256766e-01 9.46933150e-01 -1.14023018e+00 -8.27246249e-01
-3.80689412e-01 -9.49528694e-01 -7.59137034e-01 -3.22669327e-01
-3.19029301e-01 9.61637616e-01 7.11648464e-01 1.72402546... | [5.01560115814209, 2.215137004852295] |
a8b7ca8a-a415-4ed3-822a-9211cd666985 | synthpop-a-hybrid-framework-for-generating-a | 2304.12284 | null | https://arxiv.org/abs/2304.12284v1 | https://arxiv.org/pdf/2304.12284v1.pdf | Synthpop++: A Hybrid Framework for Generating A Country-scale Synthetic Population | Population censuses are vital to public policy decision-making. They provide insight into human resources, demography, culture, and economic structure at local, regional, and national levels. However, such surveys are very expensive (especially for low and middle-income countries with high populations, such as India), ... | ['Debayan Gupta', 'Kshitij Kapoor', 'Bhavesh Neekhra'] | 2023-04-24 | null | null | null | null | ['culture'] | ['speech'] | [-2.04639629e-01 1.86008308e-02 -1.60840824e-02 -5.42851686e-02
-4.12412405e-01 -3.68142307e-01 8.03115726e-01 5.54446220e-01
-5.93157768e-01 1.29356039e+00 4.49299216e-01 -4.62085247e-01
1.71089143e-01 -1.51844192e+00 -4.60129499e-01 -5.46574116e-01
-2.68416315e-01 8.25540304e-01 -6.66183606e-02 -2.23424658... | [6.311021327972412, 4.295563220977783] |
e4713e68-bcee-42a8-8fdf-b6bc18902d57 | sentiment-recognition-of-italian-elderly | 2211.07307 | null | https://arxiv.org/abs/2211.07307v1 | https://arxiv.org/pdf/2211.07307v1.pdf | Sentiment recognition of Italian elderly through domain adaptation on cross-corpus speech dataset | The aim of this work is to define a speech emotion recognition (SER) model able to recognize positive, neutral and negative emotions in natural conversations of Italian elderly people. Several datasets for SER are available in the literature. However most of them are in English or Chinese, have been recorded while acto... | ['Alessandra Grossi', 'Francesca Gasparini'] | 2022-11-14 | null | null | null | null | ['cross-corpus', 'speech-emotion-recognition'] | ['computer-vision', 'speech'] | [-3.03433001e-01 4.54895765e-01 5.19280545e-02 -4.99766886e-01
-1.22669108e-01 -1.71998218e-01 1.01026309e+00 3.30469877e-01
-9.66602683e-01 1.24800074e+00 4.34561193e-01 3.33814502e-01
6.04605302e-02 -5.49742877e-01 9.23257843e-02 -5.00222266e-01
-1.26645565e-01 7.47461498e-01 1.26917893e-02 -4.75153685... | [13.162384986877441, 6.187129020690918] |
1104e36f-692d-4fff-ba0f-50f7f21ef5a9 | category-level-shape-estimation-for-densely | 2302.11983 | null | https://arxiv.org/abs/2302.11983v1 | https://arxiv.org/pdf/2302.11983v1.pdf | Category-level Shape Estimation for Densely Cluttered Objects | Accurately estimating the shape of objects in dense clutters makes important contribution to robotic packing, because the optimal object arrangement requires the robot planner to acquire shape information of all existed objects. However, the objects for packing are usually piled in dense clutters with severe occlusion,... | ['Haibin Yan', 'Jiwen Lu', 'Ziwei Wang', 'Zhenyu Wu'] | 2023-02-23 | null | null | null | null | ['point-cloud-reconstruction'] | ['computer-vision'] | [-1.95062339e-01 -1.80271506e-01 2.50453085e-01 -1.97831556e-01
-3.98229063e-01 -8.96218836e-01 -1.34376124e-01 -1.01104910e-02
8.79566669e-02 2.17452019e-01 -3.70338112e-01 2.49120414e-01
-3.16797942e-01 -7.77319312e-01 -9.22022462e-01 -9.40938115e-01
2.14745224e-01 1.21895075e+00 4.79860127e-01 -1.35482371... | [7.447391986846924, -2.6599292755126953] |
434d8350-c2aa-4775-a03e-af1b06ec5d53 | local-water-filling-algorithm-for-shadow | null | null | https://pubmed.ncbi.nlm.nih.gov/33291572/ | https://pdfs.semanticscholar.org/cd2a/cba3283f304b232ac7a26838e747d7185d5d.pdf?_gl=1*uybhf6*_ga*MTIwOTc3MTQ0OS4xNjczNjY5MDI4*_ga_H7P4ZT52H5*MTY4MzgyMjM0NS4yNS4xLjE2ODM4MjIzNTYuMC4wLjA. | Local Water-Filling Algorithm for Shadow Detection and Removal of Document Images | Shadow detection and removal is an important task for digitized document applications. It is hard for many methods to distinguish shadow from printed text due to the high darkness similarity. In this paper, we propose a local water-filling method to remove shadows by mapping a document image into a structure of topogra... | ['C L Philip Chen', 'Bingshu Wang'] | 2020-12-04 | null | null | null | sensors-2020-12 | ['shadow-removal', 'shadow-detection-and-removal', 'shadow-detection'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 7.51955092e-01 -4.00813460e-01 6.45602703e-01 -2.66712099e-01
-2.83988148e-01 -4.97804046e-01 3.47434968e-01 -1.42651439e-01
-2.16292784e-01 5.84156215e-01 9.33330208e-02 -4.57558006e-01
2.69185722e-01 -7.81876564e-01 -3.30095589e-01 -8.24758112e-01
4.69705135e-01 9.85426083e-02 8.95572841e-01 -1.50652155... | [10.832277297973633, -3.984349250793457] |
a515e93c-169e-4752-aa2a-47967770aff8 | variable-star-classification-using-multi-view | 1911.05821 | null | https://arxiv.org/abs/1911.05821v1 | https://arxiv.org/pdf/1911.05821v1.pdf | Variable Star Classification Using Multi-View Metric Learning | Our multi-view metric learning framework enables robust characterization of star categories by directly learning to discriminate in a multi-faceted feature space, thus, eliminating the need to combine feature representations prior to fitting the machine learning model. We also demonstrate how to extend standard multi-v... | ['S. M. Caballero-Nieves', 'R. Haber', 'K. B. Johnston', 'A. M. Peter', 'V. Petit'] | 2019-11-13 | null | null | null | null | ['multi-view-learning'] | ['computer-vision'] | [-1.20719165e-01 -5.45366824e-01 -4.02462512e-01 -4.07368660e-01
-8.01730692e-01 -1.20596528e+00 8.25283527e-01 -4.72955197e-01
1.07437238e-01 3.72257501e-01 2.80692309e-01 -9.19034705e-02
-5.79450309e-01 -6.25930429e-01 -2.24539250e-01 -8.29503477e-01
5.30523509e-02 6.66336298e-01 -2.80624628e-01 -2.17647925... | [8.374481201171875, 4.535530090332031] |
9e00de3b-3e6e-4bfe-a87f-461f1f94e35d | shef-multimodal-grounding-machine-translation | null | null | https://aclanthology.org/W16-2363 | https://aclanthology.org/W16-2363.pdf | SHEF-Multimodal: Grounding Machine Translation on Images | null | ['Lucia Specia', 'Kashif Shah', 'Josiah Wang'] | 2016-08-01 | null | null | null | ws-2016-8 | ['video-description', 'multimodal-machine-translation'] | ['computer-vision', '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... | [-7.363471508026123, 3.7340803146362305] |
e75e2f22-61ce-4051-a532-a33710203b65 | transact-transformer-based-realtime-user | 2306.00248 | null | https://arxiv.org/abs/2306.00248v1 | https://arxiv.org/pdf/2306.00248v1.pdf | TransAct: Transformer-based Realtime User Action Model for Recommendation at Pinterest | Sequential models that encode user activity for next action prediction have become a popular design choice for building web-scale personalized recommendation systems. Traditional methods of sequential recommendation either utilize end-to-end learning on realtime user actions, or learn user representations separately in... | ['Andrew Zhai', 'Zhiyuan Zhang', 'Nazanin Farahpour', 'Saurabh Vishwas Joshi', 'Neng Gu', 'Po-Wei Wang', 'Dhruvil Deven Badani', 'Nikil Pancha', 'Pong Eksombatchai', 'Xue Xia'] | 2023-05-31 | null | null | null | null | ['sequential-recommendation'] | ['miscellaneous'] | [ 5.55220433e-02 -1.31099641e-01 -8.43812346e-01 -5.20812988e-01
-1.00283659e+00 -6.49979115e-01 7.62596965e-01 7.54859000e-02
-1.12690859e-01 8.63015354e-02 1.05967653e+00 -2.62965858e-01
-4.63967979e-01 -7.16598809e-01 -5.37879229e-01 1.27298474e-01
-3.60623360e-01 3.11686039e-01 1.36278003e-01 -4.45064664... | [10.202327728271484, 5.692389488220215] |
0c0150ff-af37-4095-a0d7-1e0dcc5d567a | vocal-style-factorization-for-effective | 2305.07997 | null | https://arxiv.org/abs/2305.07997v1 | https://arxiv.org/pdf/2305.07997v1.pdf | Vocal Style Factorization for Effective Speaker Recognition in Affective Scenarios | The accuracy of automated speaker recognition is negatively impacted by change in emotions in a person's speech. In this paper, we hypothesize that speaker identity is composed of various vocal style factors that may be learned from unlabeled data and re-combined using a neural network architecture to generate holistic... | ['Arun Ross', 'Morgan Sandler'] | 2023-05-13 | null | null | null | null | ['speaker-recognition'] | ['speech'] | [ 1.04867995e-01 3.49311833e-03 7.47400522e-02 -1.00831378e+00
-5.76226771e-01 -7.31909394e-01 4.59669173e-01 -4.51439917e-01
-1.76779285e-01 3.40846568e-01 4.56524342e-01 1.87668040e-01
3.94446492e-01 -2.94187367e-01 -3.99312347e-01 -5.49215794e-01
1.59013629e-01 3.83514494e-01 -7.65273750e-01 -3.73241872... | [14.060338973999023, 6.023562908172607] |
5d8dbd68-6305-4f09-bf75-d474f640fe64 | patch2pix-epipolar-guided-pixel-level | 2012.01909 | null | https://arxiv.org/abs/2012.01909v3 | https://arxiv.org/pdf/2012.01909v3.pdf | Patch2Pix: Epipolar-Guided Pixel-Level Correspondences | The classical matching pipeline used for visual localization typically involves three steps: (i) local feature detection and description, (ii) feature matching, and (iii) outlier rejection. Recently emerged correspondence networks propose to perform those steps inside a single network but suffer from low matching resol... | ['Laura Leal-Taixe', 'Torsten Sattler', 'Qunjie Zhou'] | 2020-12-03 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Zhou_Patch2Pix_Epipolar-Guided_Pixel-Level_Correspondences_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Zhou_Patch2Pix_Epipolar-Guided_Pixel-Level_Correspondences_CVPR_2021_paper.pdf | cvpr-2021-1 | ['homography-estimation'] | ['computer-vision'] | [-3.35414074e-02 -1.93718672e-01 -1.01601884e-01 -3.27416599e-01
-9.63881552e-01 -4.84489202e-01 5.23151577e-01 3.01945150e-01
-4.32837993e-01 2.03751162e-01 3.89235727e-02 9.77731645e-02
2.02785030e-01 -6.26816690e-01 -1.16066504e+00 -3.77400815e-01
1.67671487e-01 5.41743219e-01 4.63489980e-01 8.64724964... | [8.074464797973633, -2.2149860858917236] |
105b84c6-81bf-4b86-80fe-473a013b2fc1 | a2-efficient-automated-attacker-for-boosting | 2210.03543 | null | https://arxiv.org/abs/2210.03543v2 | https://arxiv.org/pdf/2210.03543v2.pdf | A2: Efficient Automated Attacker for Boosting Adversarial Training | Based on the significant improvement of model robustness by AT (Adversarial Training), various variants have been proposed to further boost the performance. Well-recognized methods have focused on different components of AT (e.g., designing loss functions and leveraging additional unlabeled data). It is generally accep... | ['Yihua Huang', 'Ming Gu', 'Weiqiang Wang', 'ZhenZhe Ying', 'Shiwen Cui', 'Changhua Meng', 'Guanghui Zhu', 'Zhuoer Xu'] | 2022-10-07 | null | null | null | null | ['adversarial-defense'] | ['adversarial'] | [ 1.05026364e-01 1.79689787e-02 -1.41630694e-01 -2.06826240e-01
-1.14195597e+00 -1.16970813e+00 4.91816789e-01 -6.23136312e-02
-2.32221529e-01 5.46961367e-01 -2.08774939e-01 -4.78092968e-01
6.38819337e-02 -7.69550741e-01 -7.54399657e-01 -8.44280958e-01
7.28826001e-02 2.82902449e-01 3.06408852e-01 -2.52636224... | [5.770592212677002, 7.839046478271484] |
8fd0ec76-23e2-4024-bef4-0e5f2036fb90 | whu-stereo-a-challenging-benchmark-for-stereo | 2206.02342 | null | https://arxiv.org/abs/2206.02342v1 | https://arxiv.org/pdf/2206.02342v1.pdf | WHU-Stereo: A Challenging Benchmark for Stereo Matching of High-Resolution Satellite Images | Stereo matching of high-resolution satellite images (HRSI) is still a fundamental but challenging task in the field of photogrammetry and remote sensing. Recently, deep learning (DL) methods, especially convolutional neural networks (CNNs), have demonstrated tremendous potential for stereo matching on public benchmark ... | ['Lin Zhang', 'Wanshou Jiang', 'San Jiang', 'Sheng He', 'Shenhong Li'] | 2022-06-06 | null | null | null | null | ['stereo-matching-1'] | ['computer-vision'] | [ 1.58215612e-01 -5.18882453e-01 -1.21935541e-02 -5.45832932e-01
-7.76623547e-01 -1.70382913e-02 5.43499708e-01 -5.29134989e-01
-5.06809294e-01 6.71525240e-01 1.49062827e-01 -5.14946640e-01
-1.05375804e-01 -1.55268717e+00 -8.79364014e-01 -6.73917830e-01
1.74766071e-02 4.96710390e-01 -2.25706268e-02 -5.61609805... | [8.876594543457031, -2.260822057723999] |
f3eb9c2c-21f4-495c-9c6b-74d3d205752b | dual-flow-fusion-model-for-concrete-surface | 2305.05132 | null | https://arxiv.org/abs/2305.05132v2 | https://arxiv.org/pdf/2305.05132v2.pdf | Dual flow fusion model for concrete surface crack segmentation | The existence of cracks and other damages pose a significant threat to the safe operation of transportation infrastructure. Traditional manual detection and ultrasound equipment testing consume a lot of time and resources. With the development of deep learning technology, many deep learning models have been widely appl... | ['Yuwei Duan'] | 2023-05-09 | null | null | null | null | ['crack-segmentation'] | ['computer-vision'] | [-1.00893132e-01 -5.14067948e-01 -3.27759087e-02 -1.75796196e-01
-5.23666263e-01 6.26416579e-02 -7.88444653e-02 1.12218158e-02
-5.19252956e-01 1.72779739e-01 -4.12064791e-01 -6.78505003e-02
2.85327341e-02 -1.12776637e+00 -3.43339026e-01 -1.13133717e+00
2.45510668e-01 6.62891939e-02 8.29569936e-01 1.24638779... | [7.496574401855469, 1.5137207508087158] |
a3360027-7e7d-4e5e-85f7-b598cab1a8c4 | cutting-music-source-separation-some-slakh-a | 1909.08494 | null | https://arxiv.org/abs/1909.08494v1 | https://arxiv.org/pdf/1909.08494v1.pdf | Cutting Music Source Separation Some Slakh: A Dataset to Study the Impact of Training Data Quality and Quantity | Music source separation performance has greatly improved in recent years with the advent of approaches based on deep learning. Such methods typically require large amounts of labelled training data, which in the case of music consist of mixtures and corresponding instrument stems. However, stems are unavailable for mos... | ['Prem Seetharaman', 'Gordon Wichern', 'Ethan Manilow', 'Jonathan Le Roux'] | 2019-09-18 | null | null | null | null | ['music-source-separation'] | ['music'] | [ 2.74667889e-01 -3.02892745e-01 2.15393398e-02 1.54768720e-01
-1.32539070e+00 -8.56537759e-01 3.95779610e-01 -6.97488189e-02
-2.33238578e-01 5.09023190e-01 4.84240055e-01 -6.31338283e-02
-5.35108507e-01 -3.85690659e-01 -4.13978428e-01 -6.88789546e-01
-3.62604409e-02 5.59999168e-01 -3.58526438e-01 -2.63547570... | [15.676801681518555, 5.4136199951171875] |
280532c2-3fe3-4ceb-875a-6687d8e05e96 | few-shot-open-set-learning-for-on-device | 2306.02161 | null | https://arxiv.org/abs/2306.02161v1 | https://arxiv.org/pdf/2306.02161v1.pdf | Few-Shot Open-Set Learning for On-Device Customization of KeyWord Spotting Systems | A personalized KeyWord Spotting (KWS) pipeline typically requires the training of a Deep Learning model on a large set of user-defined speech utterances, preventing fast customization directly applied on-device. To fill this gap, this paper investigates few-shot learning methods for open-set KWS classification by combi... | ['Tinne Tuytelaars', 'Manuele Rusci'] | 2023-06-03 | null | null | null | null | ['open-set-learning', 'keyword-spotting'] | ['miscellaneous', 'speech'] | [ 2.91345924e-01 4.31693465e-01 -1.43346742e-01 -7.24403203e-01
-1.14606047e+00 -2.34345645e-01 4.48456198e-01 1.04320824e-01
-7.40922511e-01 4.99300748e-01 -1.07734345e-01 -5.32801151e-01
5.34299351e-02 -4.04546291e-01 -7.70650923e-01 -4.68152910e-01
-4.48445603e-02 5.74528337e-01 3.29237729e-01 -1.77530378... | [14.211549758911133, 6.474434852600098] |
f4f0b6fa-fc31-46f3-8d32-bf636ce7143f | brain-tumor-recurrence-vs-radiation-necrosis | 2306.03270 | null | https://arxiv.org/abs/2306.03270v1 | https://arxiv.org/pdf/2306.03270v1.pdf | Brain Tumor Recurrence vs. Radiation Necrosis Classification and Patient Survivability Prediction | GBM (Glioblastoma multiforme) is the most aggressive type of brain tumor in adults that has a short survival rate even after aggressive treatment with surgery and radiation therapy. The changes on magnetic resonance imaging (MRI) for patients with GBM after radiotherapy are indicative of either radiation-induced necros... | ['K. M. Iftekharuddin', 'A. Vossough', 'E. Lappinen', 'A. Temtam', 'W. Farzana', 'M. S. Sadique'] | 2023-06-05 | null | null | null | null | ['synthetic-data-generation', 'synthetic-data-generation'] | ['medical', 'miscellaneous'] | [ 3.32370669e-01 4.24704142e-02 -4.59759012e-02 -3.67278993e-01
-1.06600380e+00 -1.48140669e-01 5.96224308e-01 4.21075553e-01
-5.05744159e-01 1.05558372e+00 5.42865038e-01 -4.02979463e-01
-3.88676226e-01 -7.82156467e-01 -1.64466307e-01 -1.02544260e+00
-1.91527307e-01 6.28650665e-01 -6.09360524e-02 1.22579999... | [14.765338897705078, -2.572373151779175] |
f1495b40-d8d5-4b35-a69a-f22664625ca0 | xsim-an-improved-proxy-to-bitext-mining | 2306.12907 | null | https://arxiv.org/abs/2306.12907v1 | https://arxiv.org/pdf/2306.12907v1.pdf | xSIM++: An Improved Proxy to Bitext Mining Performance for Low-Resource Languages | We introduce a new proxy score for evaluating bitext mining based on similarity in a multilingual embedding space: xSIM++. In comparison to xSIM, this improved proxy leverages rule-based approaches to extend English sentences in any evaluation set with synthetic, hard-to-distinguish examples which more closely mirror t... | ['Holger Schwenk', 'Alex Mourachko', 'Onur Çelebi', 'Kevin Heffernan', 'Mingda Chen'] | 2023-06-22 | null | null | null | null | ['nmt'] | ['computer-code'] | [ 2.19371051e-01 7.75127932e-02 -4.59458798e-01 -6.35242820e-01
-1.32799816e+00 -8.32586944e-01 8.46001923e-01 1.83830678e-01
-7.55355179e-01 7.21949816e-01 3.80542874e-01 -9.04738069e-01
1.19914729e-02 -7.12999105e-01 -7.49335825e-01 6.24621287e-03
7.88165182e-02 6.46879315e-01 -3.91609609e-01 -4.20692146... | [11.533024787902832, 10.259288787841797] |
e6ff2e46-3fce-4c69-ac5c-a9c552340edf | plan-then-generate-controlled-data-to-text | 2108.13740 | null | https://arxiv.org/abs/2108.13740v1 | https://arxiv.org/pdf/2108.13740v1.pdf | Plan-then-Generate: Controlled Data-to-Text Generation via Planning | Recent developments in neural networks have led to the advance in data-to-text generation. However, the lack of ability of neural models to control the structure of generated output can be limiting in certain real-world applications. In this study, we propose a novel Plan-then-Generate (PlanGen) framework to improve th... | ['Nigel Collier', 'Yimai Fang', 'Sihui Wang', 'David Vandyke', 'Yixuan Su'] | 2021-08-31 | null | https://aclanthology.org/2021.findings-emnlp.76 | https://aclanthology.org/2021.findings-emnlp.76.pdf | findings-emnlp-2021-11 | ['data-to-text-generation'] | ['natural-language-processing'] | [ 4.02988523e-01 4.29714233e-01 -9.36524104e-03 -3.66925150e-01
-5.91532350e-01 -6.88309669e-01 9.86758411e-01 -5.19671850e-02
-9.31289271e-02 1.09179962e+00 5.04236102e-01 -1.62052721e-01
1.15287185e-01 -1.06895816e+00 -5.99201202e-01 -3.75711769e-01
3.70746493e-01 4.85532373e-01 -1.26744092e-01 -5.59004188... | [11.677658081054688, 9.026005744934082] |
a1ce9495-7e50-4256-851e-d41f9ff9e7c8 | fast-kinodynamic-planning-on-the-constraint | 2301.04330 | null | https://arxiv.org/abs/2301.04330v2 | https://arxiv.org/pdf/2301.04330v2.pdf | Fast Kinodynamic Planning on the Constraint Manifold with Deep Neural Networks | Motion planning is a mature area of research in robotics with many well-established methods based on optimization or sampling the state space, suitable for solving kinematic motion planning. However, when dynamic motions under constraints are needed and computation time is limited, fast kinodynamic planning on the cons... | ['Jan Peters', 'Piotr Skrzypczyński', 'Krzysztof Walas', 'Haitham Bou-Ammar', 'Davide Tateo', 'Puze Liu', 'Piotr Kicki'] | 2023-01-11 | null | null | null | null | ['motion-planning'] | ['robots'] | [ 1.49968714e-01 1.81465492e-01 -4.14975911e-01 8.12372640e-02
-1.48474351e-01 -4.18642938e-01 5.05896032e-01 -1.01660348e-01
-7.92082250e-01 7.29665041e-01 -1.53780773e-01 -2.97139108e-01
-6.06376052e-01 -5.66024840e-01 -6.18339360e-01 -7.51231670e-01
-3.33150268e-01 8.53632271e-01 3.93464714e-01 -5.31388640... | [4.850027561187744, 1.2667981386184692] |
cf85a7c8-73c4-4ba7-8755-7e6e5bc832b9 | turning-flowchart-into-dialog-plan-based-data | 2305.01323 | null | https://arxiv.org/abs/2305.01323v2 | https://arxiv.org/pdf/2305.01323v2.pdf | Turning Flowchart into Dialog: Plan-based Data Augmentation for Low-Resource Flowchart-grounded Troubleshooting Dialogs | Flowchart-grounded troubleshooting dialogue (FTD) systems, which follow the instructions of a flowchart to diagnose users' problems in specific domains (eg., vehicle, laptop), have been gaining research interest in recent years. However, collecting sufficient dialogues that are naturally grounded on flowcharts is costl... | ['Gholamreza Haffari', 'YuFei Wang', 'Ingrid Zukerman', 'Lizhen Qu', 'Sameen Maruf', 'Haolan Zhan'] | 2023-05-02 | null | null | null | null | ['response-generation'] | ['natural-language-processing'] | [ 3.55056971e-02 5.28207123e-01 -3.49082053e-01 -5.83678484e-01
-1.05162799e+00 -6.10632718e-01 6.62588775e-01 -1.22670792e-01
1.57733887e-01 9.64573741e-01 8.49649489e-01 -4.46239263e-01
1.44041628e-01 -6.39362574e-01 -1.34626329e-01 -1.58894539e-01
3.77094120e-01 9.47105050e-01 -2.71179467e-01 -5.93527317... | [12.785566329956055, 8.10977554321289] |
8dccbca6-d7dc-4f35-81e1-5704533addb6 | incremental-few-shot-object-detection-via | 2302.09779 | null | https://arxiv.org/abs/2302.09779v1 | https://arxiv.org/pdf/2302.09779v1.pdf | Incremental Few-Shot Object Detection via Simple Fine-Tuning Approach | In this paper, we explore incremental few-shot object detection (iFSD), which incrementally learns novel classes using only a few examples without revisiting base classes. Previous iFSD works achieved the desired results by applying meta-learning. However, meta-learning approaches show insufficient performance that is ... | ['Jong-Hwan Kim', 'Tae-Min Choi'] | 2023-02-20 | null | null | null | null | ['few-shot-object-detection'] | ['computer-vision'] | [ 2.58975297e-01 2.84229219e-03 -3.49975526e-01 -2.31379136e-01
-9.40508485e-01 -9.26192924e-02 5.58659375e-01 6.75408691e-02
-4.56442535e-01 8.54406655e-01 -1.96884856e-01 9.20861140e-02
-1.60657331e-01 -7.95311928e-01 -5.52344143e-01 -7.51128078e-01
2.32114658e-01 2.58188725e-01 1.06979072e+00 3.66707779... | [9.664093971252441, 2.4786529541015625] |
2f13c7f5-0a35-4356-a78d-edb4810b70ee | directtracker-3d-multi-object-tracking-using | 2209.14965 | null | https://arxiv.org/abs/2209.14965v1 | https://arxiv.org/pdf/2209.14965v1.pdf | DirectTracker: 3D Multi-Object Tracking Using Direct Image Alignment and Photometric Bundle Adjustment | Direct methods have shown excellent performance in the applications of visual odometry and SLAM. In this work we propose to leverage their effectiveness for the task of 3D multi-object tracking. To this end, we propose DirectTracker, a framework that effectively combines direct image alignment for the short-term tracki... | ['Daniel Cremers', 'Laura Leal-Taixé', 'Aljoša Ošep', 'Nikolaus Demmel', 'Nikita Korobov', 'Mariia Gladkova'] | 2022-09-29 | null | null | null | null | ['3d-multi-object-tracking'] | ['computer-vision'] | [-3.28893065e-01 -5.72285116e-01 -1.18926845e-01 -1.20775200e-01
-7.89636552e-01 -7.33699083e-01 8.99203002e-01 2.28341103e-01
-5.79823315e-01 1.85539559e-01 -3.41047794e-01 -1.66746527e-01
4.33972813e-02 -2.53791004e-01 -6.56414211e-01 -4.99108016e-01
-1.13656573e-01 6.82135284e-01 8.32433641e-01 -1.20342098... | [6.640886306762695, -2.2243950366973877] |
7c58e776-5334-4354-abb8-74a8dd439bfa | 190910122 | 1909.10122 | null | https://arxiv.org/abs/1909.10122v1 | https://arxiv.org/pdf/1909.10122v1.pdf | Towards Best Experiment Design for Evaluating Dialogue System Output | To overcome the limitations of automated metrics (e.g. BLEU, METEOR) for evaluating dialogue systems, researchers typically use human judgments to provide convergent evidence. While it has been demonstrated that human judgments can suffer from the inconsistency of ratings, extant research has also found that the design... | ['Sashank Santhanam', 'Samira Shaikh'] | 2019-09-23 | towards-best-experiment-design-for-evaluating | https://aclanthology.org/W19-8610 | https://aclanthology.org/W19-8610.pdf | ws-2019-10 | ['dialogue-evaluation'] | ['natural-language-processing'] | [-1.57526448e-01 2.85916805e-01 6.59250021e-02 -7.89051056e-01
-7.57930875e-01 -1.06538153e+00 7.59906888e-01 4.56700683e-01
-1.06728458e+00 5.93569815e-01 6.57322943e-01 -5.01543939e-01
1.59093991e-01 -2.50582039e-01 1.16733350e-01 2.54974604e-01
4.93468672e-01 3.28867137e-01 1.57924071e-01 -4.00480479... | [12.778443336486816, 8.06870174407959] |
13f72c29-3588-47cc-a4cc-39b71622ce7b | hierarchy-of-visual-words-a-learning-based | 1908.02786 | null | https://arxiv.org/abs/1908.02786v1 | https://arxiv.org/pdf/1908.02786v1.pdf | Hierarchy-of-Visual-Words: a Learning-based Approach for Trademark Image Retrieval | In this paper, we present the Hierarchy-of-Visual-Words (HoVW), a novel trademark image retrieval (TIR) method that decomposes images into simpler geometric shapes and defines a descriptor for binary trademark image representation by encoding the hierarchical arrangement of component shapes. The proposed hierarchical o... | ['Vítor N. Lourenço', 'Leandro A. F. Fernandes', 'Gabriela G. Silva'] | 2019-08-07 | null | null | null | null | ['trademark-retrieval'] | ['computer-vision'] | [-2.21462920e-01 -4.16628003e-01 -2.77507961e-01 -2.32400805e-01
-4.07788038e-01 -1.13062453e+00 8.76416504e-01 5.52712142e-01
-1.13897726e-01 -5.36892116e-02 7.75101110e-02 -1.91249818e-01
-3.60527277e-01 -7.47034907e-01 -4.21507359e-01 -5.00497043e-01
-2.45773867e-01 6.17454946e-01 5.98833382e-01 -2.08898127... | [10.551675796508789, 0.04197406768798828] |
941f687f-892b-41aa-84cf-ed5f6262e6ca | latent-space-models-for-multiplex-networks | 2012.14409 | null | https://arxiv.org/abs/2012.14409v2 | https://arxiv.org/pdf/2012.14409v2.pdf | Latent space models for multiplex networks with shared structure | Latent space models are frequently used for modeling single-layer networks and include many popular special cases, such as the stochastic block model and the random dot product graph. However, they are not well-developed for more complex network structures, which are becoming increasingly common in practice. Here we pr... | ['Ji Zhu', 'Elizaveta Levina', 'Peter W. MacDonald'] | 2020-12-28 | null | null | null | null | ['stochastic-block-model'] | ['graphs'] | [ 3.01101536e-01 1.58220023e-01 -4.76681262e-01 -1.14220984e-01
2.15432093e-01 -7.43926764e-01 5.76394141e-01 -3.09219003e-01
3.15228179e-02 6.67337298e-01 1.30915150e-01 -1.62990257e-01
-8.53696167e-01 -7.85476625e-01 -6.20042920e-01 -1.02983224e+00
-5.54691732e-01 7.16668963e-01 1.43183768e-01 2.47604668... | [7.0004425048828125, 5.249223232269287] |
fa6b090d-a368-4745-8428-e0cfffc564b3 | manipulating-visually-aware-federated | 2305.08183 | null | https://arxiv.org/abs/2305.08183v2 | https://arxiv.org/pdf/2305.08183v2.pdf | Manipulating Visually-aware Federated Recommender Systems and Its Countermeasures | Federated recommender systems (FedRecs) have been widely explored recently due to their ability to protect user data privacy. In FedRecs, a central server collaboratively learns recommendation models by sharing model public parameters with clients, thereby offering a privacy-preserving solution. Unfortunately, the expo... | ['Chaoqun Yang', 'Hongzhi Yin', 'Quoc Viet Hung Nguyen', 'Shilong Yuan', 'Wei Yuan'] | 2023-05-14 | null | null | null | null | ['collaborative-filtering'] | ['miscellaneous'] | [-1.09670490e-01 -2.63010740e-01 -1.06768608e-01 -1.03370450e-01
-4.11153674e-01 -1.53959548e+00 2.83872396e-01 -5.15939593e-01
-1.83302656e-01 1.44717574e-01 1.90107614e-01 -5.57955921e-01
1.94428280e-01 -1.10941947e+00 -9.42110062e-01 -8.31876576e-01
6.81049302e-02 -2.39284575e-01 -1.18488297e-01 -1.27194643... | [5.820383071899414, 7.1865386962890625] |
20a72d15-63bf-4298-a0dc-7ff9ba69a301 | higher-order-neural-additive-models-an | 2209.15409 | null | https://arxiv.org/abs/2209.15409v1 | https://arxiv.org/pdf/2209.15409v1.pdf | Higher-order Neural Additive Models: An Interpretable Machine Learning Model with Feature Interactions | Black-box models, such as deep neural networks, exhibit superior predictive performances, but understanding their behavior is notoriously difficult. Many explainable artificial intelligence methods have been proposed to reveal the decision-making processes of black box models. However, their applications in high-stakes... | ['Jinho Kim', 'Hyun-Soo Choi', 'Minkyu Kim'] | 2022-09-30 | null | null | null | null | ['additive-models'] | ['methodology'] | [ 1.35822132e-01 5.28150856e-01 -4.97813374e-01 -6.29899502e-01
-3.24544728e-01 -1.20660588e-01 2.90499538e-01 -2.32061386e-01
-1.36739880e-01 7.29205787e-01 6.55827224e-02 -5.96524596e-01
-4.73915637e-01 -7.75387645e-01 -7.72446156e-01 -5.94251752e-01
1.66507587e-01 4.89734143e-01 -2.63282299e-01 -1.86118394... | [8.928467750549316, 5.656005382537842] |
2151da96-0bbf-4b0c-bbf6-acc2312338e2 | a-survey-on-datasets-for-decision-making-of | 2306.16784 | null | https://arxiv.org/abs/2306.16784v1 | https://arxiv.org/pdf/2306.16784v1.pdf | A Survey on Datasets for Decision-making of Autonomous Vehicle | Autonomous vehicles (AV) are expected to reshape future transportation systems, and decision-making is one of the critical modules toward high-level automated driving. To overcome those complicated scenarios that rule-based methods could not cope with well, data-driven decision-making approaches have aroused more and m... | ['Jianqiang Wang', 'Shaobing Xu', 'Yining Xing', 'Zeyu Han', 'Yuning Wang'] | 2023-06-29 | null | null | null | null | ['autonomous-vehicles', 'decision-making'] | ['computer-vision', 'reasoning'] | [ 4.45918404e-02 -1.19425036e-01 -7.41916955e-01 -9.09067690e-01
-2.69568950e-01 -5.21248102e-01 7.42607832e-01 3.55459809e-01
-3.65288585e-01 6.75378621e-01 8.89273807e-02 -6.49660051e-01
-3.33837628e-01 -1.08994126e+00 -3.39006573e-01 -5.79386950e-01
4.70357507e-01 3.52095485e-01 4.77165073e-01 -5.87589502... | [5.700277328491211, 1.1486294269561768] |
5786df5e-cd41-4302-b77a-23b7e9638cf3 | learning-stationary-markov-processes-with | 2303.05497 | null | https://arxiv.org/abs/2303.05497v2 | https://arxiv.org/pdf/2303.05497v2.pdf | Learning Stationary Markov Processes with Contrastive Adjustment | We introduce a new optimization algorithm, termed contrastive adjustment, for learning Markov transition kernels whose stationary distribution matches the data distribution. Contrastive adjustment is not restricted to a particular family of transition distributions and can be used to model data in both continuous and d... | ['Joakim Lundeberg', 'Jens Lagergren', 'Ludvig Bergenstråhle'] | 2023-03-09 | null | null | null | null | ['image-inpainting'] | ['computer-vision'] | [ 3.26506138e-01 4.12058644e-02 -2.92292327e-01 -3.83392982e-02
-7.59744406e-01 -4.28302348e-01 1.08158219e+00 -2.91545063e-01
-7.12996125e-02 4.90963817e-01 3.26847285e-01 -2.60733455e-01
-8.76469314e-02 -6.59016609e-01 -7.22214460e-01 -7.08520055e-01
2.34369785e-01 7.51905322e-01 2.05690265e-01 3.41330953... | [11.261992454528809, -0.20386356115341187] |
bba7c842-827f-4202-8eaf-b8b6ff757e22 | juncnet-a-deep-neural-network-for-road | 1809.01011 | null | http://arxiv.org/abs/1809.01011v1 | http://arxiv.org/pdf/1809.01011v1.pdf | JuncNet: A Deep Neural Network for Road Junction Disambiguation for Autonomous Vehicles | With a great amount of research going on in the field of autonomous vehicles
or self-driving cars, there has been considerable progress in road detection
and tracking algorithms. Most of these algorithms use GPS to handle road
junctions and its subsequent decisions. However, there are places in the urban
environment wh... | ['S. N. Omkar', 'Navaneethkrishnan B', 'Sumedh Mannar', 'Saumya Kumaar'] | 2018-08-31 | null | null | null | null | ['junction-detection'] | ['computer-vision'] | [ 2.25890335e-03 1.44003376e-01 -1.30956411e-01 -4.42314744e-01
-2.91046858e-01 -3.27390492e-01 9.68340516e-01 -5.29867932e-02
-6.62023783e-01 7.82152355e-01 -3.00559700e-01 -1.02651846e+00
-4.61305911e-03 -1.46217597e+00 -6.37944639e-01 -3.99965763e-01
5.61823323e-02 2.50603735e-01 7.16479301e-01 -5.14508069... | [8.668585777282715, -1.186037302017212] |
2ef7c070-5573-4e05-8a37-1c1b8c38bd96 | mean-covariance-robust-risk-measurement | 2112.09959 | null | https://arxiv.org/abs/2112.09959v1 | https://arxiv.org/pdf/2112.09959v1.pdf | Mean-Covariance Robust Risk Measurement | We introduce a universal framework for mean-covariance robust risk measurement and portfolio optimization. We model uncertainty in terms of the Gelbrich distance on the mean-covariance space, along with prior structural information about the population distribution. Our approach is related to the theory of optimal tran... | ['Daniel Kuhn', 'Damir Filipović', 'Soroosh Shafieezadeh Abadeh', 'Viet Anh Nguyen'] | 2021-12-18 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [-1.50541067e-01 1.27032906e-01 -1.80674121e-01 -4.10018623e-01
-1.11770368e+00 -6.47031367e-01 5.40491760e-01 1.24741644e-02
-4.93212610e-01 8.56024444e-01 4.09400225e-01 -5.70175111e-01
-1.10622036e+00 -8.94815326e-01 -4.60779101e-01 -7.86220491e-01
-3.54711920e-01 5.00847936e-01 -2.37743989e-01 -6.95545897... | [5.00227689743042, 3.9218759536743164] |
d307f0a8-65f3-4da5-8581-5d3133904abc | privacy-preserving-deep-learning-based-record | 2211.02161 | null | https://arxiv.org/abs/2211.02161v1 | https://arxiv.org/pdf/2211.02161v1.pdf | Privacy-preserving Deep Learning based Record Linkage | Deep learning-based linkage of records across different databases is becoming increasingly useful in data integration and mining applications to discover new insights from multiple sources of data. However, due to privacy and confidentiality concerns, organisations often are not willing or allowed to share their sensit... | ['Ming Ding', 'Dinusha Vatsalan', 'Thilina Ranbaduge'] | 2022-11-03 | null | null | null | null | ['data-integration', 'privacy-preserving-deep-learning', 'privacy-preserving-deep-learning'] | ['knowledge-base', 'methodology', 'natural-language-processing'] | [ 8.23380649e-02 9.74853560e-02 -2.41503850e-01 -6.67700827e-01
-9.87164199e-01 -1.09859169e+00 3.31471056e-01 1.07812846e+00
-5.58791399e-01 7.38883555e-01 1.44368196e-02 -5.17293453e-01
-3.85738075e-01 -1.22396886e+00 -9.08920825e-01 -6.36824608e-01
-2.81409681e-01 7.51014471e-01 -4.47249562e-02 1.06790043... | [5.944058895111084, 6.740748405456543] |
2ba47d88-f1b1-4581-98e0-82bef866895b | analyzing-and-simulating-user-utterance | 2205.01763 | null | https://arxiv.org/abs/2205.01763v1 | https://arxiv.org/pdf/2205.01763v1.pdf | Analyzing and Simulating User Utterance Reformulation in Conversational Recommender Systems | User simulation has been a cost-effective technique for evaluating conversational recommender systems. However, building a human-like simulator is still an open challenge. In this work, we focus on how users reformulate their utterances when a conversational agent fails to understand them. First, we perform a user stud... | ['Krisztian Balog', 'Mu-Chun Wang', 'Shuo Zhang'] | 2022-05-03 | null | null | null | null | ['user-simulation'] | ['natural-language-processing'] | [ 2.74296284e-01 3.77869964e-01 4.01534140e-01 -5.86508751e-01
-4.40141469e-01 -8.65654826e-01 1.04134810e+00 1.57662764e-01
-1.31521061e-01 6.56085134e-01 5.44210494e-01 -5.52975953e-01
4.51830029e-02 -6.04959726e-01 -3.30592722e-01 -1.23342872e-01
1.90834939e-01 9.34818089e-01 1.00234471e-01 -6.61205888... | [12.600794792175293, 7.8681182861328125] |
70ba3fa4-aa67-4b2d-b974-b39dc1c6d0db | codesearchnet-challenge-evaluating-the-state | 1909.09436 | null | https://arxiv.org/abs/1909.09436v3 | https://arxiv.org/pdf/1909.09436v3.pdf | CodeSearchNet Challenge: Evaluating the State of Semantic Code Search | Semantic code search is the task of retrieving relevant code given a natural language query. While related to other information retrieval tasks, it requires bridging the gap between the language used in code (often abbreviated and highly technical) and natural language more suitable to describe vague concepts and ideas... | ['Miltiadis Allamanis', 'Ho-Hsiang Wu', 'Tiferet Gazit', 'Marc Brockschmidt', 'Hamel Husain'] | 2019-09-20 | null | null | null | null | ['code-search', 'code-search'] | ['computer-code', 'computer-vision'] | [-1.68871641e-01 1.85223326e-01 -3.09526622e-01 -3.30720156e-01
-1.04241538e+00 -1.07440710e+00 5.07758558e-01 4.97777611e-01
-2.22987548e-01 3.38674575e-01 6.12882018e-01 -6.52113616e-01
-1.43248841e-01 -2.97096372e-01 -4.37225819e-01 1.01652361e-01
-1.67915821e-01 2.31190622e-01 5.38188875e-01 -2.52185911... | [7.542995929718018, 8.072410583496094] |
b33d31d7-7d26-4b88-a4b6-0f4e52479f5a | little-ball-of-fur-a-python-library-for-graph | 2006.04311 | null | https://arxiv.org/abs/2006.04311v2 | https://arxiv.org/pdf/2006.04311v2.pdf | Little Ball of Fur: A Python Library for Graph Sampling | Sampling graphs is an important task in data mining. In this paper, we describe Little Ball of Fur a Python library that includes more than twenty graph sampling algorithms. Our goal is to make node, edge, and exploration-based network sampling techniques accessible to a large number of professionals, researchers, and ... | ['Oliver Kiss', 'Benedek Rozemberczki', 'Rik Sarkar'] | 2020-06-08 | null | null | null | cikm-2020-10 | ['graph-sampling'] | ['graphs'] | [-1.85108602e-01 3.44141304e-01 -5.09548962e-01 -2.58902282e-01
-1.64239734e-01 -5.48094630e-01 5.52967250e-01 3.51017565e-01
-1.21762976e-01 6.11545146e-01 1.48205683e-01 -6.99749589e-01
-3.22253555e-01 -1.23478639e+00 -2.16359437e-01 -1.62437320e-01
-6.32106304e-01 6.10432506e-01 3.81507814e-01 -1.74580440... | [6.97621488571167, 5.955612659454346] |
48496ba0-749e-48a8-bd38-ac75367c5d3e | is-disentanglement-enough-on-latent | 2108.01450 | null | https://arxiv.org/abs/2108.01450v1 | https://arxiv.org/pdf/2108.01450v1.pdf | Is Disentanglement enough? On Latent Representations for Controllable Music Generation | Improving controllability or the ability to manipulate one or more attributes of the generated data has become a topic of interest in the context of deep generative models of music. Recent attempts in this direction have relied on learning disentangled representations from data such that the underlying factors of varia... | ['Alexander Lerch', 'Ashis Pati'] | 2021-08-01 | null | null | null | null | ['music-generation', 'music-generation'] | ['audio', 'music'] | [ 1.69539496e-01 2.18192354e-01 -2.53022939e-01 -2.91735172e-01
-4.16188151e-01 -9.14760709e-01 9.72782373e-01 -1.91313669e-01
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-3.41748804e-01 -7.73724973e-01 -7.50284612e-01 -9.40710485e-01
2.52406657e-01 4.44014549e-01 -4.29584146e-01 -2.96009660... | [9.265371322631836, 4.876946926116943] |
3d171170-7af2-4b83-9481-c746a5109638 | genetic-analysis-of-prostate-cancer-with | 2303.15851 | null | https://arxiv.org/abs/2303.15851v2 | https://arxiv.org/pdf/2303.15851v2.pdf | Genetic Analysis of Prostate Cancer with Computer Science Methods | Metastatic prostate cancer is one of the most common cancers in men. In the advanced stages of prostate cancer, tumours can metastasise to other tissues in the body, which is fatal. In this thesis, we performed a genetic analysis of prostate cancer tumours at different metastatic sites using data science, machine learn... | ['Shi Zhou', 'YuXuan Li'] | 2023-03-28 | null | null | null | null | ['community-detection', 'tumour-classification'] | ['graphs', 'medical'] | [ 2.49723434e-01 7.49787763e-02 -6.38748631e-02 -1.23420656e-02
-3.87445062e-01 -4.59647417e-01 4.45988178e-01 6.72366619e-01
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-7.43300438e-01 7.38777816e-01 -1.67718939e-02 -1.47690505... | [6.399196624755859, 5.49188232421875] |
d8eb2392-979e-429d-b193-5d8517208ec2 | reconnet-non-iterative-reconstruction-of-1 | null | null | http://openaccess.thecvf.com/content_cvpr_2016/html/Kulkarni_ReconNet_Non-Iterative_Reconstruction_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Kulkarni_ReconNet_Non-Iterative_Reconstruction_CVPR_2016_paper.pdf | ReconNet: Non-Iterative Reconstruction of Images From Compressively Sensed Measurements | The goal of this paper is to present a non-iterative and more importantly an extremely fast algorithm to reconstruct images from compressively sensed (CS) random measurements. To this end, we propose a novel convolutional neural network (CNN) architecture which takes in CS measurements of an image as input and outputs... | ['Amit Ashok', 'Suhas Lohit', 'Pavan Turaga', 'Ronan Kerviche', 'Kuldeep Kulkarni'] | 2016-06-01 | null | null | null | cvpr-2016-6 | ['real-time-visual-tracking'] | ['computer-vision'] | [ 9.47330773e-01 -1.37882575e-01 1.75523475e-01 -1.09781161e-01
-9.25403297e-01 -5.57206750e-01 3.92852694e-01 -2.99595237e-01
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-2.51408160e-01 -1.07076196e-02 2.03854755e-01 -1.12563446... | [10.977230072021484, -2.2230710983276367] |
d4e0f59e-7813-4581-9d58-29a2a1d5fe8a | transformers-are-deep-infinite-dimensional-1 | 2106.01506 | null | https://arxiv.org/abs/2106.01506v1 | https://arxiv.org/pdf/2106.01506v1.pdf | Transformers are Deep Infinite-Dimensional Non-Mercer Binary Kernel Machines | Despite their ubiquity in core AI fields like natural language processing, the mechanics of deep attention-based neural networks like the Transformer model are not fully understood. In this article, we present a new perspective towards understanding how Transformers work. In particular, we show that the "dot-product at... | ['Joseph E. Gonzalez', 'Matthew A. Wright'] | 2021-06-02 | transformers-are-deep-infinite-dimensional | https://openreview.net/forum?id=AVKFuhH1Fo4 | https://openreview.net/pdf?id=AVKFuhH1Fo4 | null | ['deep-attention', 'deep-attention'] | ['computer-vision', 'natural-language-processing'] | [ 1.53620895e-02 2.40131676e-01 -7.60823265e-02 -5.24317741e-01
-2.40512297e-01 -5.73394537e-01 4.49088991e-01 -7.46693090e-02
-4.59527731e-01 5.39087415e-01 3.13249156e-02 -6.73327744e-01
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-4.26839978e-01 4.27592903e-01 -5.67178093e-02 -3.07715029... | [7.53745698928833, 3.857351064682007] |
2e82b8dd-7386-45bd-9d9b-bff961a85b5b | inflated-3d-convolution-transformer-for | 2306.02548 | null | https://arxiv.org/abs/2306.02548v3 | https://arxiv.org/pdf/2306.02548v3.pdf | Inflated 3D Convolution-Transformer for Weakly-supervised Carotid Stenosis Grading with Ultrasound Videos | Localization of the narrowest position of the vessel and corresponding vessel and remnant vessel delineation in carotid ultrasound (US) are essential for carotid stenosis grading (CSG) in clinical practice. However, the pipeline is time-consuming and tough due to the ambiguous boundaries of plaque and temporal variatio... | ['Dong Ni', 'Jie Ren', 'Jia Liu', 'Yuanji Zhang', 'Qilong Ying', 'Yuxin Zou', 'Xin Yang', 'Wufeng Xue', 'Yuhao Huang', 'Xinrui Zhou'] | 2023-06-05 | null | null | null | null | ['video-classification'] | ['computer-vision'] | [ 2.91795693e-02 -1.08138241e-01 -1.34052476e-02 -3.97867292e-01
-1.13040698e+00 -7.86000490e-01 2.40839005e-01 -1.61065817e-01
-3.44525695e-01 5.02178252e-01 1.62154779e-01 -7.72159159e-01
-9.29062963e-02 -3.49305809e-01 -4.90398198e-01 -7.28258550e-01
-2.74617940e-01 1.22516051e-01 4.26859677e-01 7.15674311... | [14.50469970703125, -2.457000255584717] |
5dd5f8b2-54ac-4b0d-8ec5-e156bdd518c7 | neural-koopman-pooling-control-inspired | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wang_Neural_Koopman_Pooling_Control-Inspired_Temporal_Dynamics_Encoding_for_Skeleton-Based_Action_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wang_Neural_Koopman_Pooling_Control-Inspired_Temporal_Dynamics_Encoding_for_Skeleton-Based_Action_CVPR_2023_paper.pdf | Neural Koopman Pooling: Control-Inspired Temporal Dynamics Encoding for Skeleton-Based Action Recognition | Skeleton-based human action recognition is becoming increasingly important in a variety of fields. Most existing works train a CNN or GCN based backbone to extract spatial-temporal features, and use temporal average/max pooling to aggregate the information. However, these pooling methods fail to capture high-order ... | ['Yadong Mu', 'Xin Xu', 'Xinghan Wang'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['skeleton-based-action-recognition', 'action-recognition-in-videos', 'action-recognition'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-2.42083427e-02 -4.91824478e-01 -4.61538434e-01 1.08107015e-01
-3.03597420e-01 -1.18920133e-01 5.76514065e-01 -3.57275635e-01
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-2.61762202e-01 -1.68378547e-01 4.92854327e-01 -2.74918020... | [7.862645149230957, 0.32347196340560913] |
ec2273f1-b54d-412f-a699-a5418083869e | efficient-long-sequential-user-data-modeling | 2209.12212 | null | https://arxiv.org/abs/2209.12212v1 | https://arxiv.org/pdf/2209.12212v1.pdf | Efficient Long Sequential User Data Modeling for Click-Through Rate Prediction | Recent studies on Click-Through Rate (CTR) prediction has reached new levels by modeling longer user behavior sequences. Among others, the two-stage methods stand out as the state-of-the-art (SOTA) solution for industrial applications. The two-stage methods first train a retrieval model to truncate the long behavior se... | ['Junfeng Ge', 'Tao Zhuang', 'Shanshan Lv', 'Changhua Pei', 'Yue Xu', 'Qiwei Chen'] | 2022-09-25 | null | null | null | null | ['click-through-rate-prediction'] | ['miscellaneous'] | [-1.97301269e-01 -8.02719951e-01 -4.59701240e-01 -2.58829117e-01
-8.47964406e-01 -3.56000692e-01 1.26646757e-01 -3.17811891e-02
-1.85531780e-01 1.76153764e-01 -2.83697158e-01 -6.99379325e-01
-1.01263873e-01 -7.03081548e-01 -5.75317860e-01 -5.76860607e-01
-8.14708173e-02 4.25428092e-01 1.99711695e-01 -5.56435287... | [10.098097801208496, 5.572655200958252] |
d046b0d8-9371-4133-8396-7688eae29bd2 | estimation-of-control-area-in-badminton | 2305.04247 | null | https://arxiv.org/abs/2305.04247v1 | https://arxiv.org/pdf/2305.04247v1.pdf | Estimation of control area in badminton doubles with pose information from top and back view drone videos | The application of visual tracking to the performance analysis of sports players in dynamic competitions is vital for effective coaching. In racket sports, most previous studies have focused on analyzing and assessing singles players without occlusion in broadcast videos and discrete representations (e.g., stroke) that... | ['Keisuke Fujii', 'Yingjiu Bei', 'Wenhui Jin', 'Kazuya Takeda', 'Ning Ding'] | 2023-05-07 | null | null | null | null | ['visual-tracking'] | ['computer-vision'] | [-1.93111882e-01 -2.13526055e-01 -6.30446672e-02 -2.70538926e-02
-4.97133762e-01 -7.84555376e-01 9.77096856e-02 2.42722794e-01
-6.32709801e-01 3.54709178e-01 1.28651336e-01 1.55792058e-01
-4.62775707e-01 -7.93399036e-01 -7.53826916e-01 -4.18568760e-01
-3.69855225e-01 6.08830154e-01 7.58215964e-01 -7.23271072... | [7.387075901031494, 0.0937662124633789] |
f2d09df0-9fdf-4b17-ac23-886cfe8f41b9 | detecting-and-grounding-multi-modal-media | 2304.02556 | null | https://arxiv.org/abs/2304.02556v1 | https://arxiv.org/pdf/2304.02556v1.pdf | Detecting and Grounding Multi-Modal Media Manipulation | Misinformation has become a pressing issue. Fake media, in both visual and textual forms, is widespread on the web. While various deepfake detection and text fake news detection methods have been proposed, they are only designed for single-modality forgery based on binary classification, let alone analyzing and reasoni... | ['Ziwei Liu', 'Tianxing Wu', 'Rui Shao'] | 2023-04-05 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Shao_Detecting_and_Grounding_Multi-Modal_Media_Manipulation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Shao_Detecting_and_Grounding_Multi-Modal_Media_Manipulation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['face-swapping', 'misinformation', 'fake-news-detection'] | ['computer-vision', 'miscellaneous', 'natural-language-processing'] | [ 2.73359835e-01 -3.16185623e-01 -3.71483475e-01 1.98460013e-01
-1.11255860e+00 -5.98636210e-01 1.00580239e+00 2.02194368e-03
8.45183805e-02 2.61475205e-01 4.99930531e-01 -5.69168404e-02
3.57742667e-01 -7.83309221e-01 -1.05360258e+00 -5.29932201e-01
2.76215345e-01 3.70239094e-02 3.16686958e-01 -5.48576593... | [8.175990104675293, 10.300833702087402] |
5f13d07c-91a2-4109-88fe-77ca12845f7a | deep-transfer-learning-for-land-use-land | 2110.02580 | null | https://arxiv.org/abs/2110.02580v3 | https://arxiv.org/pdf/2110.02580v3.pdf | Deep Transfer Learning for Land Use and Land Cover Classification: A Comparative Study | Efficiently implementing remote sensing image classification with high spatial resolution imagery can provide a significant value in Land Use and Land Cover (LULC) classification. The new advances in remote sensing and deep learning technologies have facilitated the extraction of spatiotemporal information for LULC cla... | ['Ebrahim Ghaderpour', 'Tarunpreet Kaur', 'Raoof Naushad'] | 2021-10-06 | null | null | null | null | ['remote-sensing-image-classification'] | ['miscellaneous'] | [ 2.41354436e-01 -2.53719419e-01 -1.90246001e-01 -4.45184082e-01
-5.13069570e-01 -2.23675266e-01 6.66292727e-01 -1.96077347e-01
-6.53157890e-01 1.14985323e+00 -3.57794799e-02 -6.39053345e-01
-2.65113294e-01 -1.28886175e+00 -5.37628412e-01 -9.51216698e-01
-4.58380580e-01 -8.45368430e-02 -7.98830315e-02 -4.93853658... | [9.52055835723877, -1.5041202306747437] |
a4c311ac-785b-4de9-b6f0-7006221ff49b | optimizing-yolov7-for-semiconductor-defect | 2302.09565 | null | https://arxiv.org/abs/2302.09565v1 | https://arxiv.org/pdf/2302.09565v1.pdf | Optimizing YOLOv7 for Semiconductor Defect Detection | The field of object detection using Deep Learning (DL) is constantly evolving with many new techniques and models being proposed. YOLOv7 is a state-of-the-art object detector based on the YOLO family of models which have become popular for industrial applications. One such possible application domain can be semiconduct... | ['Stefan De Gendt', 'Sandip Halder', 'Bappaditya Dey', 'Enrique Dehaerne'] | 2023-02-19 | null | null | null | null | ['defect-detection'] | ['computer-vision'] | [-1.43803313e-01 -1.86964601e-01 1.62720695e-01 1.27580658e-01
-4.38928068e-01 5.65988419e-04 3.80943716e-01 7.14320466e-02
-3.14379841e-01 4.39117342e-01 -5.79516292e-01 -1.64063275e-01
-1.18263260e-01 -6.48743391e-01 -4.90623116e-01 -8.96764517e-01
2.51672447e-01 4.07276243e-01 1.11355007e+00 -1.88552842... | [8.540609359741211, -0.5122748613357544] |
8b6d5583-657a-4aa3-95f4-a258429f9f7b | sketch-ground-and-refine-top-down-dense-video | null | null | http://openaccess.thecvf.com//content/CVPR2021/html/Deng_Sketch_Ground_and_Refine_Top-Down_Dense_Video_Captioning_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Deng_Sketch_Ground_and_Refine_Top-Down_Dense_Video_Captioning_CVPR_2021_paper.pdf | Sketch, Ground, and Refine: Top-Down Dense Video Captioning | The dense video captioning task aims to detect and describe a sequence of events in a video for detailed and coherent storytelling. Previous works mainly adopt a "detect-then-describe" framework, which firstly detects event proposals in the video and then generates descriptions for the detected events. However, the... | ['Qi Wu', 'Yuan He', 'Da Chen', 'ShiZhe Chen', 'Chaorui Deng'] | 2021-06-19 | null | null | null | cvpr-2021-1 | ['dense-video-captioning'] | ['computer-vision'] | [ 4.25895631e-01 3.73641402e-02 -2.59360999e-01 -4.28651154e-01
-1.07990134e+00 -5.19466877e-01 8.60504329e-01 2.12749869e-01
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3.43714654e-01 -7.04670370e-01 -9.93719757e-01 -4.53410268e-01
2.89132178e-01 5.10730922e-01 5.61145663e-01 -1.60125762... | [10.424288749694824, 0.6832104921340942] |
6af22d0d-1e31-4a3b-9107-e41951ab6970 | stock-price-prediction-using-sentiment | 2204.05783 | null | https://arxiv.org/abs/2204.05783v1 | https://arxiv.org/pdf/2204.05783v1.pdf | Stock Price Prediction using Sentiment Analysis and Deep Learning for Indian Markets | Stock market prediction has been an active area of research for a considerable period. Arrival of computing, followed by Machine Learning has upgraded the speed of research as well as opened new avenues. As part of this research study, we aimed to predict the future stock movement of shares using the historical prices ... | ['Yogesh Agarwal', 'Usha Aiyer', 'Pranali Bhagat', 'Nutan Hindlekar', 'Milind Manjrekar', 'Himank Sharma', 'Anwesh Reddy Paduri', 'Narayana Darapaneni'] | 2022-04-07 | null | null | null | null | ['stock-market-prediction', 'stock-price-prediction'] | ['time-series', 'time-series'] | [-5.61038017e-01 -1.52765006e-01 -1.79000348e-01 -1.66953534e-01
-8.49337876e-03 -7.38403916e-01 6.96759403e-01 -1.77093148e-02
-2.79683053e-01 1.26385927e+00 2.78422207e-01 -5.89020610e-01
4.53710817e-02 -1.19169593e+00 -2.61034191e-01 -5.94963014e-01
9.14025307e-02 2.04771906e-01 1.78228870e-01 -3.90739232... | [4.499501705169678, 4.224732398986816] |
797987c9-5c79-4caa-bbb5-0557924c8921 | corelm-coreference-aware-language-model-fine-1 | 2111.02687 | null | https://arxiv.org/abs/2111.02687v1 | https://arxiv.org/pdf/2111.02687v1.pdf | CoreLM: Coreference-aware Language Model Fine-Tuning | Language Models are the underpin of all modern Natural Language Processing (NLP) tasks. The introduction of the Transformers architecture has contributed significantly into making Language Modeling very effective across many NLP task, leading to significant advancements in the field. However, Transformers come with a b... | ['Ioannis Vlahavas', 'Nikolaos Stylianou'] | 2021-11-04 | corelm-coreference-aware-language-model-fine | https://aclanthology.org/2021.crac-1.8 | https://aclanthology.org/2021.crac-1.8.pdf | crac-acl-2021-11 | ['lambada'] | ['natural-language-processing'] | [-1.95180446e-01 3.93049687e-01 -1.14575580e-01 -5.55454552e-01
-7.50348449e-01 -5.92691541e-01 6.24642551e-01 4.12801117e-01
-8.25614929e-01 5.78550220e-01 5.21751523e-01 -2.88204074e-01
-7.08640590e-02 -8.53330076e-01 -6.21453702e-01 -4.13207650e-01
1.39450222e-01 8.88213873e-01 3.53766441e-01 -3.86283875... | [10.425049781799316, 9.085943222045898] |
338597ab-a3fb-4389-99dc-09a031c6e5ad | not-just-plain-text-fuel-document-level | 2211.05343 | null | https://arxiv.org/abs/2211.05343v2 | https://arxiv.org/pdf/2211.05343v2.pdf | Not Just Plain Text! Fuel Document-Level Relation Extraction with Explicit Syntax Refinement and Subsentence Modeling | Document-level relation extraction (DocRE) aims to identify semantic labels among entities within a single document. One major challenge of DocRE is to dig decisive details regarding a specific entity pair from long text. However, in many cases, only a fraction of text carries required information, even in the manually... | ['Jianyong Wang', 'Zhuo Wang', 'Zhenyu Li', 'Xiuxing Li', 'Zhichao Duan'] | 2022-11-10 | null | null | null | null | ['document-level-relation-extraction'] | ['natural-language-processing'] | [-2.30618613e-03 3.74775767e-01 -7.49823749e-01 -2.43181989e-01
-7.05887616e-01 -8.25255096e-01 7.61597455e-01 7.07463503e-01
-1.61995605e-01 1.15214825e+00 4.98941690e-01 -4.64331061e-01
-4.89736855e-01 -1.07904315e+00 -4.71974641e-01 -6.37990236e-02
8.54742751e-02 5.78984797e-01 4.26518291e-01 -2.83387303... | [9.352190017700195, 8.601242065429688] |
77e3e147-d6a1-43db-8349-38e42ef02a04 | gaze-estimation-with-eye-region-segmentation | 2112.07878 | null | https://arxiv.org/abs/2112.07878v1 | https://arxiv.org/pdf/2112.07878v1.pdf | Gaze Estimation with Eye Region Segmentation and Self-Supervised Multistream Learning | We present a novel multistream network that learns robust eye representations for gaze estimation. We first create a synthetic dataset containing eye region masks detailing the visible eyeball and iris using a simulator. We then perform eye region segmentation with a U-Net type model which we later use to generate eye ... | ['Ali Etemad', 'Paul Hungler', 'Zunayed Mahmud'] | 2021-12-15 | null | null | null | null | ['gaze-estimation'] | ['computer-vision'] | [ 1.55099899e-01 1.83958039e-01 -1.75692037e-01 -6.68227017e-01
-1.73272446e-01 -3.32388222e-01 4.28950101e-01 -5.89337587e-01
-4.77635711e-01 5.76266110e-01 6.86141849e-02 -1.21888839e-01
3.37249249e-01 -1.58080906e-01 -9.87603545e-01 -3.44504446e-01
3.54408994e-02 -3.17542404e-02 3.14962596e-01 -1.23448104... | [14.122995376586914, 0.04828708991408348] |
2acdf16d-0c20-426a-adcf-c41c9f9cd5ab | crossner-evaluating-cross-domain-named-entity | 2012.04373 | null | https://arxiv.org/abs/2012.04373v2 | https://arxiv.org/pdf/2012.04373v2.pdf | CrossNER: Evaluating Cross-Domain Named Entity Recognition | Cross-domain named entity recognition (NER) models are able to cope with the scarcity issue of NER samples in target domains. However, most of the existing NER benchmarks lack domain-specialized entity types or do not focus on a certain domain, leading to a less effective cross-domain evaluation. To address these obsta... | ['Pascale Fung', 'Andrea Madotto', 'Samuel Cahyawijaya', 'Ziwei Ji', 'Wenliang Dai', 'Tiezheng Yu', 'Yan Xu', 'Zihan Liu'] | 2020-12-08 | null | null | null | null | ['cross-domain-named-entity-recognition'] | ['natural-language-processing'] | [-2.77971715e-01 -2.33983487e-01 -1.65956110e-01 -4.57906455e-01
-8.79915357e-01 -1.05948520e+00 6.69088840e-01 2.07910195e-01
-9.36889231e-01 8.97137344e-01 3.56312484e-01 -1.00022823e-01
1.52359381e-01 -9.03505266e-01 -4.88405704e-01 -1.76314175e-01
2.25406930e-01 5.72002828e-01 3.62588972e-01 -4.99603152... | [9.784720420837402, 9.516398429870605] |
be76be30-8515-4c29-8d2c-21809b85f19c | context-based-emotion-recognition-using | 2003.13401 | null | https://arxiv.org/abs/2003.13401v1 | https://arxiv.org/pdf/2003.13401v1.pdf | Context Based Emotion Recognition using EMOTIC Dataset | In our everyday lives and social interactions we often try to perceive the emotional states of people. There has been a lot of research in providing machines with a similar capacity of recognizing emotions. From a computer vision perspective, most of the previous efforts have been focusing in analyzing the facial expre... | ['Ronak Kosti', 'Agata Lapedriza', 'Adria Recasens', 'Jose M. Alvarez'] | 2020-03-30 | null | null | null | null | ['emotion-recognition-in-context'] | ['natural-language-processing'] | [-1.78749070e-01 -2.54402697e-01 6.26382679e-02 -8.94365609e-01
6.40863925e-02 -4.62551504e-01 3.82064193e-01 1.67876825e-01
-3.58299464e-01 4.31773365e-01 4.19658691e-01 4.49619859e-01
2.45473623e-01 -4.39210802e-01 6.43480048e-02 -5.80545962e-01
2.12526321e-02 5.70636988e-02 -3.60687852e-01 -4.99623835... | [13.488564491271973, 2.218980550765991] |
81406312-fd7a-431e-9d0a-f6ec2b000a87 | frequency-aware-interface-dynamics-with | null | null | https://openreview.net/forum?id=uMDbGsVjCS4 | https://openreview.net/pdf?id=uMDbGsVjCS4 | Frequency-aware Interface Dynamics with Generative Adversarial Networks | We present a new method for reconstructing and refining complex surfaces based on physical simulations. Taking a roughly approximated simulation as input, our method infers corresponding spatial details while taking into account how they evolve over time. We consider this problem in terms of spatial and temporal freq... | ['Nils Thuerey', 'Jan Bender', 'Tassilo Kugelstadt', 'Lukas Prantl'] | 2021-01-01 | null | null | null | null | ['physical-simulations'] | ['miscellaneous'] | [ 2.88404256e-01 2.36607015e-01 4.88565177e-01 1.50495037e-01
-6.46912158e-01 -8.08608234e-01 8.02562356e-01 -3.21235180e-01
-2.38470688e-01 9.18815374e-01 -8.77987370e-02 -2.11377576e-01
-1.91178054e-01 -1.23522782e+00 -1.06095719e+00 -8.20245385e-01
-4.31416482e-01 5.64755380e-01 3.21311265e-01 -4.85631734... | [6.506822109222412, 3.3827428817749023] |
0f5aeb98-926a-4e61-a350-221d02120122 | zero-memory-optimization-towards-training-a | 1910.02054 | null | https://arxiv.org/abs/1910.02054v3 | https://arxiv.org/pdf/1910.02054v3.pdf | ZeRO: Memory Optimizations Toward Training Trillion Parameter Models | Large deep learning models offer significant accuracy gains, but training billions to trillions of parameters is challenging. Existing solutions such as data and model parallelisms exhibit fundamental limitations to fit these models into limited device memory, while obtaining computation, communication and development ... | ['Jeff Rasley', 'Samyam Rajbhandari', 'Olatunji Ruwase', 'Yuxiong He'] | 2019-10-04 | null | null | null | null | ['cross-lingual-document-classification'] | ['natural-language-processing'] | [-3.87014419e-01 -2.17873096e-01 -3.92509133e-01 -5.27937114e-01
-8.45405340e-01 -5.73469758e-01 1.46236554e-01 1.05715618e-01
-8.48966479e-01 4.62432474e-01 -4.11133498e-01 -9.29228425e-01
3.45116258e-02 -7.78480113e-01 -9.30696309e-01 -3.84171456e-01
1.33550197e-01 9.06985700e-01 3.01913247e-02 -2.44034216... | [8.566521644592285, 3.3390743732452393] |
55e46439-a32a-4390-85bb-2584fb3078e5 | contextualized-topic-coherence-metrics | 2305.14587 | null | https://arxiv.org/abs/2305.14587v1 | https://arxiv.org/pdf/2305.14587v1.pdf | Contextualized Topic Coherence Metrics | The recent explosion in work on neural topic modeling has been criticized for optimizing automated topic evaluation metrics at the expense of actual meaningful topic identification. But human annotation remains expensive and time-consuming. We propose LLM-based methods inspired by standard human topic evaluations, in a... | ['Bernd Amann', 'Camelia Constantin', 'Hubert Naacke', 'David Mimno', 'Jacob Louis Hoover', 'Hamed Rahimi'] | 2023-05-23 | null | null | null | null | ['topic-models'] | ['natural-language-processing'] | [ 1.17166005e-01 4.88167018e-01 -2.86061168e-01 -5.58912814e-01
-1.52083790e+00 -6.27571762e-01 1.09740269e+00 6.31503403e-01
-5.01470268e-01 7.00213611e-01 5.32510817e-01 -2.31514201e-01
-3.18849176e-01 -4.96864438e-01 -6.61533624e-02 -5.21879315e-01
-1.94229990e-01 1.00363183e+00 2.69507885e-01 1.82527184... | [10.375534057617188, 7.0937700271606445] |
c1fdb1e4-40dd-4dcb-ac13-4442179dbc22 | elastic-boundary-projection-for-3d-medical | 1812.00518 | null | https://arxiv.org/abs/1812.00518v2 | https://arxiv.org/pdf/1812.00518v2.pdf | Elastic Boundary Projection for 3D Medical Image Segmentation | We focus on an important yet challenging problem: using a 2D deep network to deal with 3D segmentation for medical image analysis. Existing approaches either applied multi-view planar (2D) networks or directly used volumetric (3D) networks for this purpose, but both of them are not ideal: 2D networks cannot capture 3D ... | ['Alan L. Yuille', 'Huangjie Zheng', 'Elliot K. Fishman', 'Lingxi Xie', 'Tianwei Ni'] | 2018-12-03 | elastic-boundary-projection-for-3d-medical-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Ni_Elastic_Boundary_Projection_for_3D_Medical_Image_Segmentation_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Ni_Elastic_Boundary_Projection_for_3D_Medical_Image_Segmentation_CVPR_2019_paper.pdf | cvpr-2019-6 | ['3d-medical-imaging-segmentation'] | ['medical'] | [-1.11782692e-01 2.91556954e-01 -7.00902194e-02 -6.20220117e-02
-3.76842797e-01 -6.10937655e-01 1.35697767e-01 5.79023808e-02
-2.71344870e-01 2.87960470e-01 5.04981466e-02 -3.30240130e-01
1.98750034e-01 -9.98461008e-01 -7.89122105e-01 -6.31764770e-01
-1.49954529e-02 8.67644429e-01 4.80053395e-01 -7.66562596... | [14.52917194366455, -2.445671319961548] |
d2b078ba-80ad-41c4-81a5-50de7eb7ba6e | twin-contrastive-learning-with-noisy-labels | 2303.06930 | null | https://arxiv.org/abs/2303.06930v1 | https://arxiv.org/pdf/2303.06930v1.pdf | Twin Contrastive Learning with Noisy Labels | Learning from noisy data is a challenging task that significantly degenerates the model performance. In this paper, we present TCL, a novel twin contrastive learning model to learn robust representations and handle noisy labels for classification. Specifically, we construct a Gaussian mixture model (GMM) over the repre... | ['Hongming Shan', 'Junping Zhang', 'Zhizhong Huang'] | 2023-03-13 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Huang_Twin_Contrastive_Learning_With_Noisy_Labels_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Huang_Twin_Contrastive_Learning_With_Noisy_Labels_CVPR_2023_paper.pdf | cvpr-2023-1 | ['learning-with-noisy-labels', 'learning-with-noisy-labels'] | ['computer-vision', 'natural-language-processing'] | [-1.08213527e-02 6.95763007e-02 -2.34198481e-01 -8.84384096e-01
-1.66432834e+00 -4.08181399e-01 3.31835240e-01 -4.09485161e-01
-2.52672434e-01 6.68545067e-01 -2.32752301e-02 -1.13786608e-01
2.02556461e-01 -2.95611203e-01 -7.45561421e-01 -9.96042669e-01
1.60052374e-01 4.04146165e-01 -1.99418932e-01 3.08745772... | [9.405890464782715, 3.897252082824707] |
d86ff52f-4f19-4160-ab88-ea706c30e51f | coordx-accelerating-implicit-neural-1 | 2201.12425 | null | https://arxiv.org/abs/2201.12425v1 | https://arxiv.org/pdf/2201.12425v1.pdf | CoordX: Accelerating Implicit Neural Representation with a Split MLP Architecture | Implicit neural representations with multi-layer perceptrons (MLPs) have recently gained prominence for a wide variety of tasks such as novel view synthesis and 3D object representation and rendering. However, a significant challenge with these representations is that both training and inference with an MLP over a larg... | ['Nandita Vijaykumar', 'Hongyi Sun', 'Ruofan Liang'] | 2022-01-28 | coordx-accelerating-implicit-neural | https://openreview.net/forum?id=oAy7yPmdNz | https://openreview.net/pdf?id=oAy7yPmdNz | iclr-2022-4 | ['3d-shape-representation'] | ['computer-vision'] | [ 3.98259640e-01 1.27527297e-01 7.51577318e-02 -3.60554039e-01
-7.28033245e-01 -2.24551320e-01 7.01330721e-01 2.01050262e-03
-1.56686202e-01 4.87809449e-01 -3.29888165e-02 -2.25177079e-01
2.47330904e-01 -8.62810910e-01 -1.14572167e+00 -8.29464257e-01
8.37707072e-02 3.03879380e-01 -2.51625534e-02 2.20886827... | [11.086041450500488, -1.822986364364624] |
6b1c0674-ba5b-403d-aa09-d69cbed2b2bc | deepimagespam-deep-learning-based-image-spam | 1810.03977 | null | http://arxiv.org/abs/1810.03977v1 | http://arxiv.org/pdf/1810.03977v1.pdf | DeepImageSpam: Deep Learning based Image Spam Detection | Hackers and spammers are employing innovative and novel techniques to deceive
novice and even knowledgeable internet users. Image spam is one of such
technique where the spammer varies and changes some portion of the image such
that it is indistinguishable from the original image fooling the users. This
paper proposes ... | ['Soman Kp', 'Amara Dinesh Kumar', 'Vinayakumar R'] | 2018-10-03 | null | null | null | null | ['spam-detection'] | ['natural-language-processing'] | [ 4.47577648e-02 -2.86522537e-01 4.69265819e-01 -3.14751491e-02
3.05459172e-01 -9.02342498e-01 5.39288759e-01 1.39284924e-01
-4.00688112e-01 5.94559789e-01 -4.34391856e-01 -6.35919750e-01
3.66097033e-01 -8.77853394e-01 -6.76587701e-01 -5.15786052e-01
-2.87003126e-02 2.45982707e-01 5.44352829e-01 -2.88879097... | [7.7259321212768555, 9.91807746887207] |
f6adaa5a-d9e0-4e96-8db3-fcff68196b14 | a-probabilistic-framework-for-visual | 2301.02086 | null | https://arxiv.org/abs/2301.02086v1 | https://arxiv.org/pdf/2301.02086v1.pdf | A Probabilistic Framework for Visual Localization in Ambiguous Scenes | Visual localization allows autonomous robots to relocalize when losing track of their pose by matching their current observation with past ones. However, ambiguous scenes pose a challenge for such systems, as repetitive structures can be viewed from many distinct, equally likely camera poses, which means it is not suff... | ['Patric Jensfelt', 'Alessandro Pieropan', 'Amit Dekel', 'Leonard Bruns', 'Fereidoon Zangeneh'] | 2023-01-05 | null | null | null | null | ['visual-localization'] | ['computer-vision'] | [-9.66240019e-02 -3.46092880e-02 -3.11501324e-01 -3.12716067e-01
-7.41782606e-01 -9.29116905e-01 5.33966899e-01 -2.05642775e-01
-3.66297990e-01 7.71639228e-01 6.00949861e-02 -5.69255976e-03
7.57941008e-02 -3.75305593e-01 -1.02710092e+00 -5.96593142e-01
1.84149384e-01 7.40404367e-01 2.92484522e-01 1.91781789... | [7.747799396514893, -2.381242036819458] |
b0e87f41-e610-40dc-9b83-429c250ea520 | a-fair-experimental-comparison-of-neural | 2208.14822 | null | https://arxiv.org/abs/2208.14822v1 | https://arxiv.org/pdf/2208.14822v1.pdf | A Fair Experimental Comparison of Neural Network Architectures for Latent Representations of Multi-Omics for Drug Response Prediction | Recent years have seen a surge of novel neural network architectures for the integration of multi-omics data for prediction. Most of the architectures include either encoders alone or encoders and decoders, i.e., autoencoders of various sorts, to transform multi-omics data into latent representations. One important par... | ['Stefan Kramer', 'Tony Hauptmann'] | 2022-08-31 | null | null | null | null | ['drug-response-prediction'] | ['medical'] | [ 1.48632929e-01 -3.85616094e-01 -4.72520351e-01 -2.07271084e-01
-7.74256289e-01 -4.62610096e-01 5.37011087e-01 4.78250265e-01
-3.02488267e-01 8.91919851e-01 9.16373655e-02 -4.44114625e-01
-3.46814603e-01 -6.20999813e-01 -8.41386259e-01 -8.25480878e-01
2.08176970e-01 5.87937295e-01 -4.52600509e-01 1.77681684... | [5.972275733947754, 5.678502559661865] |
5e4173bb-d540-45cf-aee2-58baac73b8f1 | pele-scores-pelvic-x-ray-landmark-detection | 2305.04294 | null | https://arxiv.org/abs/2305.04294v2 | https://arxiv.org/pdf/2305.04294v2.pdf | PELE scores: Pelvic X-ray Landmark Detection by Pelvis Extraction and Enhancement | The pelvis, the lower part of the trunk, supports and balances the trunk. Landmark detection from a pelvic X-ray (PXR) facilitates downstream analysis and computer-assisted diagnosis and treatment of pelvic diseases. Although PXRs have the advantages of low radiation and reduced cost compared to computed tomography (CT... | ['S. Kevin Zhou', 'Jianji Wang', 'Zhiwei Cheng', 'Huijie Hu', 'Heqin Zhu', 'Shitong Shao', 'Han Li', 'Zhen Huang'] | 2023-05-07 | null | null | null | null | ['computed-tomography-ct'] | ['methodology'] | [-6.73409319e-03 2.97367632e-01 -9.26770449e-01 1.05115017e-02
-8.37566257e-01 -3.55623543e-01 2.59819686e-01 4.57649857e-01
-3.24769616e-01 4.82092798e-01 1.57227486e-01 -7.06682086e-01
-1.82061881e-01 -9.01166141e-01 -3.62312198e-01 -5.65128684e-01
-2.55432606e-01 6.34959459e-01 3.62032026e-01 7.41239861... | [14.68491268157959, -2.552248477935791] |
f2f22aad-edf1-4946-83f7-7a33246808f5 | can-boosting-with-svm-as-week-learners-help | 1604.05242 | null | http://arxiv.org/abs/1604.05242v2 | http://arxiv.org/pdf/1604.05242v2.pdf | Can Boosting with SVM as Week Learners Help? | Object recognition in images involves identifying objects with partial
occlusions, viewpoint changes, varying illumination, cluttered backgrounds.
Recent work in object recognition uses machine learning techniques SVM-KNN,
Local Ensemble Kernel Learning, Multiple Kernel Learning. In this paper, we
want to utilize SVM a... | ['Dinesh Govindaraj'] | 2016-04-18 | null | null | null | null | ['object-categorization'] | ['computer-vision'] | [-5.88397622e-01 -1.07326901e+00 -5.10730088e-01 -7.45348871e-01
-3.00687969e-01 -8.04513991e-01 4.64021295e-01 6.02549672e-01
-3.27683389e-01 7.07264960e-01 3.03962708e-01 -2.07231238e-01
-4.79585648e-01 -6.60147369e-01 -1.40635222e-01 -5.75736821e-01
-3.36051524e-01 2.32191369e-01 9.23809350e-01 -1.88409165... | [8.162426948547363, 3.9057109355926514] |
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