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7eda846f-025c-4f59-b234-0238d05d8e07 | the-npu-elevoc-personalized-speech | 2303.06811 | null | https://arxiv.org/abs/2303.06811v2 | https://arxiv.org/pdf/2303.06811v2.pdf | The NPU-Elevoc Personalized Speech Enhancement System for ICASSP2023 DNS Challenge | This paper describes our NPU-Elevoc personalized speech enhancement system (NAPSE) for the 5th Deep Noise Suppression Challenge at ICASSP 2023. Based on the superior two-stage model TEA-PSE 2.0, our system particularly explores better strategy for speaker embedding fusion, optimizes the model training pipeline, and leverages adversarial training and multi-scale loss. According to the results, our system is tied for the 1st place in the headset track (track 1) and ranked 2nd in the speakerphone track (track 2). | ['Lei Xie', 'Liangliang Peng', 'Zhihao Guo', 'Yindi Yang', 'Xiaopeng Yan'] | 2023-03-13 | null | null | null | null | ['speech-enhancement'] | ['speech'] | [-1.21980637e-01 3.06312680e-01 -3.56743038e-02 -2.12236807e-01
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350b3672-35fa-4c78-a194-155f53144e1b | gated-attentive-autoencoder-for-content-aware | 1812.02869 | null | http://arxiv.org/abs/1812.02869v1 | http://arxiv.org/pdf/1812.02869v1.pdf | Gated Attentive-Autoencoder for Content-Aware Recommendation | The rapid growth of Internet services and mobile devices provides an
excellent opportunity to satisfy the strong demand for the personalized item or
product recommendation. However, with the tremendous increase of users and
items, personalized recommender systems still face several challenging
problems: (1) the hardness of exploiting sparse implicit feedback; (2) the
difficulty of combining heterogeneous data. To cope with these challenges, we
propose a gated attentive-autoencoder (GATE) model, which is capable of
learning fused hidden representations of items' contents and binary ratings,
through a neural gating structure. Based on the fused representations, our
model exploits neighboring relations between items to help infer users'
preferences. In particular, a word-level and a neighbor-level attention module
are integrated with the autoencoder. The word-level attention learns the item
hidden representations from items' word sequences, while favoring informative
words by assigning larger attention weights. The neighbor-level attention
learns the hidden representation of an item's neighborhood by considering its
neighbors in a weighted manner. We extensively evaluate our model with several
state-of-the-art methods and different validation metrics on four real-world
datasets. The experimental results not only demonstrate the effectiveness of
our model on top-N recommendation but also provide interpretable results
attributed to the attention modules. | ['Qinglong Wang', 'Xue Liu', 'Peng Kang', 'Bin Wu', 'Chen Ma'] | 2018-12-07 | null | null | null | null | ['product-recommendation'] | ['miscellaneous'] | [ 3.21939178e-02 -3.60249877e-01 -5.55065095e-01 -5.36763310e-01
-3.90304476e-01 -1.22305676e-01 1.36699602e-01 9.99602024e-03
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21cde201-87b9-4506-891e-e2b988caa7a4 | multi-level-approach-to-accurate-and-scalable | null | null | https://openreview.net/forum?id=a4W0tSTN9Kn | https://openreview.net/pdf?id=a4W0tSTN9Kn | MULTI-LEVEL APPROACH TO ACCURATE AND SCALABLE HYPERGRAPH EMBEDDING | Many problems such as node classification and link prediction in network data
can be solved using graph embeddings, and a number of algorithms are known for constructing
such embeddings. However, it is difficult to use graphs to capture
non-binary relations such as communities of nodes. These kinds of
complex relations are expressed more naturally as hypergraphs.
While hypergraphs are a generalization of graphs, state-of-the-art graph
embedding techniques are not adequate for solving prediction and
classification tasks on large hypergraphs accurately in reasonable time.
In this paper, we introduce NetVec, a novel multi-level framework for scalable unsupervised hypergraph embedding, which outperforms state-of-the-art hypergraph embedding systems by up to
15% in accuracy.
We also show that NetVec is capable of generating high quality embeddings for real-world hypergraphs with millions of nodes and hyperedges in only a couple of minutes while existing hypergraph systems either fail for such large hypergraphs or may take days to produce the embeddings. | ['Keshav Pingali', 'Dennis Wall', 'Donya Saless', 'Sepideh Maleki'] | 2021-09-29 | null | null | null | null | ['hypergraph-embedding'] | ['graphs'] | [-2.98020512e-01 6.13098323e-01 -3.21662873e-01 -1.67429432e-01
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2c610bfb-e11f-45c7-b232-0a83cb223b18 | single-stage-multi-pose-virtual-try-on | 2211.10715 | null | https://arxiv.org/abs/2211.10715v1 | https://arxiv.org/pdf/2211.10715v1.pdf | Single Stage Multi-Pose Virtual Try-On | Multi-pose virtual try-on (MPVTON) aims to fit a target garment onto a person at a target pose. Compared to traditional virtual try-on (VTON) that fits the garment but keeps the pose unchanged, MPVTON provides a better try-on experience, but is also more challenging due to the dual garment and pose editing objectives. Existing MPVTON methods adopt a pipeline comprising three disjoint modules including a target semantic layout prediction module, a coarse try-on image generator and a refinement try-on image generator. These models are trained separately, leading to sub-optimal model training and unsatisfactory results. In this paper, we propose a novel single stage model for MPVTON. Key to our model is a parallel flow estimation module that predicts the flow fields for both person and garment images conditioned on the target pose. The predicted flows are subsequently used to warp the appearance feature maps of the person and the garment images to construct a style map. The map is then used to modulate the target pose's feature map for target try-on image generation. With the parallel flow estimation design, our model can be trained end-to-end in a single stage and is more computationally efficient, resulting in new SOTA performance on existing MPVTON benchmarks. We further introduce multi-task training and demonstrate that our model can also be applied for traditional VTON and pose transfer tasks and achieve comparable performance to SOTA specialized models on both tasks. | ['Tao Xiang', 'Yi-Zhe Song', 'Sen He'] | 2022-11-19 | null | null | null | null | ['pose-transfer', 'virtual-try-on'] | ['computer-vision', 'computer-vision'] | [ 4.33264673e-01 1.91978082e-01 4.31242064e-02 -1.46112502e-01
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40020133-7abf-4298-83ce-f7dc02cf8897 | count-based-exploration-with-neural-density | 1703.01310 | null | http://arxiv.org/abs/1703.01310v2 | http://arxiv.org/pdf/1703.01310v2.pdf | Count-Based Exploration with Neural Density Models | Bellemare et al. (2016) introduced the notion of a pseudo-count, derived from
a density model, to generalize count-based exploration to non-tabular
reinforcement learning. This pseudo-count was used to generate an exploration
bonus for a DQN agent and combined with a mixed Monte Carlo update was
sufficient to achieve state of the art on the Atari 2600 game Montezuma's
Revenge. We consider two questions left open by their work: First, how
important is the quality of the density model for exploration? Second, what
role does the Monte Carlo update play in exploration? We answer the first
question by demonstrating the use of PixelCNN, an advanced neural density model
for images, to supply a pseudo-count. In particular, we examine the intrinsic
difficulties in adapting Bellemare et al.'s approach when assumptions about the
model are violated. The result is a more practical and general algorithm
requiring no special apparatus. We combine PixelCNN pseudo-counts with
different agent architectures to dramatically improve the state of the art on
several hard Atari games. One surprising finding is that the mixed Monte Carlo
update is a powerful facilitator of exploration in the sparsest of settings,
including Montezuma's Revenge. | ['Remi Munos', 'Marc G. Bellemare', 'Georg Ostrovski', 'Aaron van den Oord'] | 2017-03-03 | count-based-exploration-with-neural-density-1 | https://icml.cc/Conferences/2017/Schedule?showEvent=839 | http://proceedings.mlr.press/v70/ostrovski17a/ostrovski17a.pdf | icml-2017-8 | ['montezumas-revenge'] | ['playing-games'] | [-1.76941916e-01 4.11116555e-02 7.45945377e-04 7.66393691e-02
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3961d2d4-a073-41c7-a4f1-34211305011c | ossid-online-self-supervised-instance | 2201.07309 | null | https://arxiv.org/abs/2201.07309v2 | https://arxiv.org/pdf/2201.07309v2.pdf | OSSID: Online Self-Supervised Instance Detection by (and for) Pose Estimation | Real-time object pose estimation is necessary for many robot manipulation algorithms. However, state-of-the-art methods for object pose estimation are trained for a specific set of objects; these methods thus need to be retrained to estimate the pose of each new object, often requiring tens of GPU-days of training for optimal performance. In this paper, we propose the OSSID framework, leveraging a slow zero-shot pose estimator to self-supervise the training of a fast detection algorithm. This fast detector can then be used to filter the input to the pose estimator, drastically improving its inference speed. We show that this self-supervised training exceeds the performance of existing zero-shot detection methods on two widely used object pose estimation and detection datasets, without requiring any human annotations. Further, we show that the resulting method for pose estimation has a significantly faster inference speed, due to the ability to filter out large parts of the image. Thus, our method for self-supervised online learning of a detector (trained using pseudo-labels from a slow pose estimator) leads to accurate pose estimation at real-time speeds, without requiring human annotations. Supplementary materials and code can be found at https://georgegu1997.github.io/OSSID/ | ['David Held', 'Brian Okorn', 'Qiao Gu'] | 2022-01-18 | null | null | null | null | ['robot-manipulation'] | ['robots'] | [ 1.24121524e-01 3.72695327e-02 -1.24185167e-01 -7.69200772e-02
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f054d9ae-d668-4ceb-8eef-b9bc14202eff | quality-constant-per-shot-encoding-by-two | 2208.10739 | null | https://arxiv.org/abs/2208.10739v1 | https://arxiv.org/pdf/2208.10739v1.pdf | Quality-Constant Per-Shot Encoding by Two-Pass Learning-based Rate Factor Prediction | Providing quality-constant streams can simultaneously guarantee user experience and prevent wasting bit-rate. In this paper, we propose a novel deep learning based two-pass encoder parameter prediction framework to decide rate factor (RF), with which encoder can output streams with constant quality. For each one-shot segment in a video, the proposed method firstly extracts spatial, temporal and pre-coding features by an ultra fast pre-process. Based on these features, a RF parameter is predicted by a deep neural network. Video encoder uses the RF to compress segment as the first encoding pass. Then VMAF quality of the first pass encoding is measured. If the quality doesn't meet target, a second pass RF prediction and encoding will be performed. With the help of first pass predicted RF and corresponding actual quality as feedback, the second pass prediction will be highly accurate. Experiments show the proposed method requires only 1.55 times encoding complexity on average, meanwhile the accuracy, that the compressed video's actual VMAF is within $\pm1$ around the target VMAF, reaches 98.88%. | ['Tianxiao Ye', 'Xiaobo Li', 'Yi Wang', 'Chunlei Cai'] | 2022-08-23 | null | null | null | null | ['parameter-prediction'] | ['miscellaneous'] | [ 2.94833720e-01 -2.09657758e-01 -6.40908539e-01 -4.25864458e-01
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3ca535c5-2c6c-4000-9822-f4a741c57f40 | learning-3d-human-dynamics-from-video | 1812.01601 | null | https://arxiv.org/abs/1812.01601v4 | https://arxiv.org/pdf/1812.01601v4.pdf | Learning 3D Human Dynamics from Video | From an image of a person in action, we can easily guess the 3D motion of the person in the immediate past and future. This is because we have a mental model of 3D human dynamics that we have acquired from observing visual sequences of humans in motion. We present a framework that can similarly learn a representation of 3D dynamics of humans from video via a simple but effective temporal encoding of image features. At test time, from video, the learned temporal representation give rise to smooth 3D mesh predictions. From a single image, our model can recover the current 3D mesh as well as its 3D past and future motion. Our approach is designed so it can learn from videos with 2D pose annotations in a semi-supervised manner. Though annotated data is always limited, there are millions of videos uploaded daily on the Internet. In this work, we harvest this Internet-scale source of unlabeled data by training our model on unlabeled video with pseudo-ground truth 2D pose obtained from an off-the-shelf 2D pose detector. Our experiments show that adding more videos with pseudo-ground truth 2D pose monotonically improves 3D prediction performance. We evaluate our model, Human Mesh and Motion Recovery (HMMR), on the recent challenging dataset of 3D Poses in the Wild and obtain state-of-the-art performance on the 3D prediction task without any fine-tuning. The project website with video, code, and data can be found at https://akanazawa.github.io/human_dynamics/. | ['Jason Y. Zhang', 'Panna Felsen', 'Angjoo Kanazawa', 'Jitendra Malik'] | 2018-12-04 | learning-3d-human-dynamics-from-video-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Kanazawa_Learning_3D_Human_Dynamics_From_Video_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Kanazawa_Learning_3D_Human_Dynamics_From_Video_CVPR_2019_paper.pdf | cvpr-2019-6 | ['3d-human-dynamics', 'human-dynamics'] | ['computer-vision', 'computer-vision'] | [-1.41466379e-01 -3.04975417e-02 -2.52058744e-01 -1.46095827e-01
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bf67765a-2b39-4043-8446-023a5d39530f | knowledge-graph-augmented-network-towards | 2201.04831 | null | https://arxiv.org/abs/2201.04831v2 | https://arxiv.org/pdf/2201.04831v2.pdf | Knowledge Graph Augmented Network Towards Multiview Representation Learning for Aspect-based Sentiment Analysis | Aspect-based sentiment analysis (ABSA) is a fine-grained task of sentiment analysis. To better comprehend long complicated sentences and obtain accurate aspect-specific information, linguistic and commonsense knowledge are generally required in this task. However, most current methods employ complicated and inefficient approaches to incorporate external knowledge, e.g., directly searching the graph nodes. Additionally, the complementarity between external knowledge and linguistic information has not been thoroughly studied. To this end, we propose a knowledge graph augmented network KGAN, which aims to effectively incorporate external knowledge with explicitly syntactic and contextual information. In particular, KGAN captures the sentiment feature representations from multiple different perspectives, i.e., context-, syntax- and knowledge-based. First, KGAN learns the contextual and syntactic representations in parallel to fully extract the semantic features. Then, KGAN integrates the knowledge graphs into the embedding space, based on which the aspect-specific knowledge representations are further obtained via an attention mechanism. Last, we propose a hierarchical fusion module to complement these multi-view representations in a local-to-global manner. Extensive experiments on five popular ABSA benchmarks demonstrate the effectiveness and robustness of our KGAN. Notably, with the help of the pretrained model of RoBERTa, KGAN achieves a new record of state-of-the-art performance among all datasets. | ['DaCheng Tao', 'Hua Jin', 'Bo Du', 'Juhua Liu', 'Liang Ding', 'Qihuang Zhong'] | 2022-01-13 | null | null | null | null | ['aspect-based-sentiment-analysis'] | ['natural-language-processing'] | [-3.78831699e-02 -1.30170599e-01 -2.18565613e-01 -6.30289376e-01
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d6772124-93f3-4a3f-8420-aa4f32279a6a | a-multi-modal-transformer-network-for-action | 2305.19624 | null | https://arxiv.org/abs/2305.19624v1 | https://arxiv.org/pdf/2305.19624v1.pdf | A Multi-Modal Transformer Network for Action Detection | This paper proposes a novel multi-modal transformer network for detecting actions in untrimmed videos. To enrich the action features, our transformer network utilizes a new multi-modal attention mechanism that computes the correlations between different spatial and motion modalities combinations. Exploring such correlations for actions has not been attempted previously. To use the motion and spatial modality more effectively, we suggest an algorithm that corrects the motion distortion caused by camera movement. Such motion distortion, common in untrimmed videos, severely reduces the expressive power of motion features such as optical flow fields. Our proposed algorithm outperforms the state-of-the-art methods on two public benchmarks, THUMOS14 and ActivityNet. We also conducted comparative experiments on our new instructional activity dataset, including a large set of challenging classroom videos captured from elementary schools. | ['Peter Youngs', 'Scott T. Acton', 'Matthew Korban'] | 2023-05-31 | null | null | null | null | ['action-detection'] | ['computer-vision'] | [ 1.43561333e-01 -4.62324828e-01 -4.64158177e-01 6.41245511e-04
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ba8afa8b-bc82-4307-b9a0-0fd80913183d | unihcp-a-unified-model-for-human-centric | 2303.02936 | null | https://arxiv.org/abs/2303.02936v4 | https://arxiv.org/pdf/2303.02936v4.pdf | UniHCP: A Unified Model for Human-Centric Perceptions | Human-centric perceptions (e.g., pose estimation, human parsing, pedestrian detection, person re-identification, etc.) play a key role in industrial applications of visual models. While specific human-centric tasks have their own relevant semantic aspect to focus on, they also share the same underlying semantic structure of the human body. However, few works have attempted to exploit such homogeneity and design a general-propose model for human-centric tasks. In this work, we revisit a broad range of human-centric tasks and unify them in a minimalist manner. We propose UniHCP, a Unified Model for Human-Centric Perceptions, which unifies a wide range of human-centric tasks in a simplified end-to-end manner with the plain vision transformer architecture. With large-scale joint training on 33 human-centric datasets, UniHCP can outperform strong baselines on several in-domain and downstream tasks by direct evaluation. When adapted to a specific task, UniHCP achieves new SOTAs on a wide range of human-centric tasks, e.g., 69.8 mIoU on CIHP for human parsing, 86.18 mA on PA-100K for attribute prediction, 90.3 mAP on Market1501 for ReID, and 85.8 JI on CrowdHuman for pedestrian detection, performing better than specialized models tailored for each task. | ['Wanli Ouyang', 'Donglian Qi', 'Fengwei Yu', 'Rui Zhao', 'Feng Zhu', 'Lei Bai', 'Shixiang Tang', 'Meilin Chen', 'Yizhou Wang', 'Yuanzheng Ci'] | 2023-03-06 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Ci_UniHCP_A_Unified_Model_for_Human-Centric_Perceptions_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Ci_UniHCP_A_Unified_Model_for_Human-Centric_Perceptions_CVPR_2023_paper.pdf | cvpr-2023-1 | ['person-re-identification', 'pedestrian-attribute-recognition', 'pedestrian-detection', 'human-part-segmentation', 'human-parsing'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [-2.27171004e-01 8.93400386e-02 -1.14981942e-01 -4.06441659e-01
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5ba89db4-9d9c-47e7-b99f-7aaac8b840bc | explaining-away-results-in-accurate-and | 1911.04169 | null | https://arxiv.org/abs/1911.04169v1 | https://arxiv.org/pdf/1911.04169v1.pdf | Explaining Away Results in Accurate and Tolerant Template Matching | Recognising and locating image patches or sets of image features is an important task underlying much work in computer vision. Traditionally this has been accomplished using template matching. However, template matching is notoriously brittle in the face of changes in appearance caused by, for example, variations in viewpoint, partial occlusion, and non-rigid deformations. This article tests a method of template matching that is more tolerant to such changes in appearance and that can, therefore, more accurately identify image patches. In traditional template matching the comparison between a template and the image is independent of the other templates. In contrast, the method advocated here takes into account the evidence provided by the image for the template at each location and the full range of alternative explanations represented by the same template at other locations and by other templates. Specifically, the proposed method of template matching is performed using a form of probabilistic inference known as "explaining away". The algorithm used to implement explaining away has previously been used to simulate several neurobiological mechanisms, and been applied to image contour detection and pattern recognition tasks. Here it is applied for the first time to image patch matching, and is shown to produce superior results in comparison to the current state-of-the-art methods. | ['M. W. Spratling'] | 2019-11-11 | null | null | null | null | ['patch-matching', 'contour-detection'] | ['computer-vision', 'computer-vision'] | [ 7.06886530e-01 1.78752854e-01 1.21689662e-01 -2.85854071e-01
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f0e51ff1-b4a6-4156-be05-ceebda62c36b | domain-translation-via-latent-space-mapping | 2212.03361 | null | https://arxiv.org/abs/2212.03361v1 | https://arxiv.org/pdf/2212.03361v1.pdf | Domain Translation via Latent Space Mapping | In this paper, we investigate the problem of multi-domain translation: given an element $a$ of domain $A$, we would like to generate a corresponding $b$ sample in another domain $B$, and vice versa. Acquiring supervision in multiple domains can be a tedious task, also we propose to learn this translation from one domain to another when supervision is available as a pair $(a,b)\sim A\times B$ and leveraging possible unpaired data when only $a\sim A$ or only $b\sim B$ is available. We introduce a new unified framework called Latent Space Mapping (\model) that exploits the manifold assumption in order to learn, from each domain, a latent space. Unlike existing approaches, we propose to further regularize each latent space using available domains by learning each dependency between pairs of domains. We evaluate our approach in three tasks performing i) synthetic dataset with image translation, ii) real-world task of semantic segmentation for medical images, and iii) real-world task of facial landmark detection. | ['Romain Herault', 'Clement Chatelain', 'Simon Bernard', 'Tsiry Mayet'] | 2022-12-06 | null | null | null | null | ['facial-landmark-detection'] | ['computer-vision'] | [ 6.85469508e-01 2.24303499e-01 -2.12143511e-01 -7.21849740e-01
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437d64ce-dac8-4e16-a896-3b2e059568ec | rec-mv-reconstructing-3d-dynamic-cloth-from-1 | 2305.14236 | null | https://arxiv.org/abs/2305.14236v2 | https://arxiv.org/pdf/2305.14236v2.pdf | REC-MV: REconstructing 3D Dynamic Cloth from Monocular Videos | Reconstructing dynamic 3D garment surfaces with open boundaries from monocular videos is an important problem as it provides a practical and low-cost solution for clothes digitization. Recent neural rendering methods achieve high-quality dynamic clothed human reconstruction results from monocular video, but these methods cannot separate the garment surface from the body. Moreover, despite existing garment reconstruction methods based on feature curve representation demonstrating impressive results for garment reconstruction from a single image, they struggle to generate temporally consistent surfaces for the video input. To address the above limitations, in this paper, we formulate this task as an optimization problem of 3D garment feature curves and surface reconstruction from monocular video. We introduce a novel approach, called REC-MV, to jointly optimize the explicit feature curves and the implicit signed distance field (SDF) of the garments. Then the open garment meshes can be extracted via garment template registration in the canonical space. Experiments on multiple casually captured datasets show that our approach outperforms existing methods and can produce high-quality dynamic garment surfaces. The source code is available at https://github.com/GAP-LAB-CUHK-SZ/REC-MV. | ['Xiaoguang Han', 'Junle Wang', 'Mutian Xu', 'Jiapeng Zhou', 'GuanYing Chen', 'Lingteng Qiu'] | 2023-05-23 | rec-mv-reconstructing-3d-dynamic-cloth-from | http://openaccess.thecvf.com//content/CVPR2023/html/Qiu_REC-MV_REconstructing_3D_Dynamic_Cloth_From_Monocular_Videos_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Qiu_REC-MV_REconstructing_3D_Dynamic_Cloth_From_Monocular_Videos_CVPR_2023_paper.pdf | cvpr-2023-1 | ['neural-rendering'] | ['computer-vision'] | [ 3.12327206e-01 -3.60245258e-01 1.33068278e-01 -1.69510618e-01
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87fb446f-cbe2-48c5-b2bf-430ddac4b302 | controllable-mixed-initiative-dialogue | 2305.04147 | null | https://arxiv.org/abs/2305.04147v1 | https://arxiv.org/pdf/2305.04147v1.pdf | Controllable Mixed-Initiative Dialogue Generation through Prompting | Mixed-initiative dialogue tasks involve repeated exchanges of information and conversational control. Conversational agents gain control by generating responses that follow particular dialogue intents or strategies, prescribed by a policy planner. The standard approach has been fine-tuning pre-trained language models to perform generation conditioned on these intents. However, these supervised generation models are limited by the cost and quality of data annotation. We instead prompt large language models as a drop-in replacement to fine-tuning on conditional generation. We formalize prompt construction for controllable mixed-initiative dialogue. Our findings show improvements over fine-tuning and ground truth responses according to human evaluation and automatic metrics for two tasks: PersuasionForGood and Emotional Support Conversations. | ['Zhou Yu', 'Urvi Awasthi', 'Weiyan Shi', 'Xiao Yu', 'Maximillian Chen'] | 2023-05-06 | null | null | null | null | ['dialogue-generation', 'dialogue-generation'] | ['natural-language-processing', 'speech'] | [ 3.11662614e-01 1.16099858e+00 -1.70433715e-01 -8.88644218e-01
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d337a078-33b9-40bb-ba29-77d8b9d49ea5 | bounding-information-leakage-in-machine | 2105.03875 | null | https://arxiv.org/abs/2105.03875v2 | https://arxiv.org/pdf/2105.03875v2.pdf | Bounding Information Leakage in Machine Learning | Recently, it has been shown that Machine Learning models can leak sensitive information about their training data. This information leakage is exposed through membership and attribute inference attacks. Although many attack strategies have been proposed, little effort has been made to formalize these problems. We present a novel formalism, generalizing membership and attribute inference attack setups previously studied in the literature and connecting them to memorization and generalization. First, we derive a universal bound on the success rate of inference attacks and connect it to the generalization gap of the target model. Second, we study the question of how much sensitive information is stored by the algorithm about its training set and we derive bounds on the mutual information between the sensitive attributes and model parameters. Experimentally, we illustrate the potential of our approach by applying it to both synthetic data and classification tasks on natural images. Finally, we apply our formalism to different attribute inference strategies, with which an adversary is able to recover the identity of writers in the PenDigits dataset. | ['Pablo Piantanida', 'Catuscia Palamidessi', 'Georg Pichler', 'Ganesh Del Grosso'] | 2021-05-09 | null | null | null | null | ['membership-inference-attack'] | ['computer-vision'] | [ 6.75024450e-01 2.80951947e-01 -5.54668903e-02 -2.91983873e-01
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d83ac179-ac16-4dae-abc2-ca30dc45d1ba | person-detection-using-an-ultra-low | 2212.08415 | null | https://arxiv.org/abs/2212.08415v1 | https://arxiv.org/pdf/2212.08415v1.pdf | Person Detection Using an Ultra Low-resolution Thermal Imager on a Low-cost MCU | Detecting persons in images or video with neural networks is a well-studied subject in literature. However, such works usually assume the availability of a camera of decent resolution and a high-performance processor or GPU to run the detection algorithm, which significantly increases the cost of a complete detection system. However, many applications require low-cost solutions, composed of cheap sensors and simple microcontrollers. In this paper, we demonstrate that even on such hardware we are not condemned to simple classic image processing techniques. We propose a novel ultra-lightweight CNN-based person detector that processes thermal video from a low-cost 32x24 pixel static imager. Trained and compressed on our own recorded dataset, our model achieves up to 91.62% accuracy (F1-score), has less than 10k parameters, and runs as fast as 87ms and 46ms on low-cost microcontrollers STM32F407 and STM32F746, respectively. | ['Toon Goedemé', 'Kristof Van Beeck', 'Wouter Reusen', 'Maarten Vandersteegen'] | 2022-12-16 | null | null | null | null | ['human-detection'] | ['computer-vision'] | [ 2.55037695e-01 -2.90301979e-01 3.01256984e-01 -2.24845439e-01
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58e32eb3-cfd7-4cc9-9592-45ce56092db9 | how-to-plant-trees-in-language-models-data | 2305.19905 | null | https://arxiv.org/abs/2305.19905v1 | https://arxiv.org/pdf/2305.19905v1.pdf | How to Plant Trees in Language Models: Data and Architectural Effects on the Emergence of Syntactic Inductive Biases | Accurate syntactic representations are essential for robust generalization in natural language. Recent work has found that pre-training can teach language models to rely on hierarchical syntactic features - as opposed to incorrect linear features - when performing tasks after fine-tuning. We test what aspects of pre-training are important for endowing encoder-decoder Transformers with an inductive bias that favors hierarchical syntactic generalizations. We focus on architectural features (depth, width, and number of parameters), as well as the genre and size of the pre-training corpus, diagnosing inductive biases using two syntactic transformation tasks: question formation and passivization, both in English. We find that the number of parameters alone does not explain hierarchical generalization: model depth plays greater role than model width. We also find that pre-training on simpler language, such as child-directed speech, induces a hierarchical bias using an order-of-magnitude less data than pre-training on more typical datasets based on web text or Wikipedia; this suggests that in cognitively plausible language acquisition settings, neural language models may be more data-efficient than previously thought. | ['Tal Linzen', 'Aaron Mueller'] | 2023-05-31 | null | null | null | null | ['language-acquisition'] | ['natural-language-processing'] | [ 1.99665442e-01 7.32217133e-01 2.40199678e-02 -7.63740301e-01
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14c81493-7912-4144-9786-b0f44449d284 | a-multibranch-convolutional-neural-network | 2208.02361 | null | https://arxiv.org/abs/2208.02361v1 | https://arxiv.org/pdf/2208.02361v1.pdf | A Multibranch Convolutional Neural Network for Hyperspectral Unmixing | Hyperspectral unmixing remains one of the most challenging tasks in the analysis of such data. Deep learning has been blooming in the field and proved to outperform other classic unmixing techniques, and can be effectively deployed onboard Earth observation satellites equipped with hyperspectral imagers. In this letter, we follow this research pathway and propose a multi-branch convolutional neural network that benefits from fusing spectral, spatial, and spectral-spatial features in the unmixing process. The results of our experiments, backed up with the ablation study, revealed that our techniques outperform others from the literature and lead to higher-quality fractional abundance estimation. Also, we investigated the influence of reducing the training sets on the capabilities of all algorithms and their robustness against noise, as capturing large and representative ground-truth sets is time-consuming and costly in practice, especially in emerging Earth observation scenarios. | ['Jakub Nalepa', 'Bertrand Le Saux', 'Nicolas Longépé', 'Michal Kawulok', 'Lukasz Tulczyjew'] | 2022-08-03 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 5.38504064e-01 -4.89873350e-01 -1.08938493e-01 7.50207677e-02
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b4155b2d-4011-451f-9ebb-577506a68726 | zyj-lt-edi-eacl2021-xlm-roberta-based-model | null | null | https://aclanthology.org/2021.ltedi-1.16 | https://aclanthology.org/2021.ltedi-1.16.pdf | ZYJ@LT-EDI-EACL2021:XLM-RoBERTa-Based Model with Attention for Hope Speech Detection | Due to the development of modern computer technology and the increase in the number of online media users, we can see all kinds of posts and comments everywhere on the internet. Hope speech can not only inspire the creators but also make other viewers pleasant. It is necessary to effectively and automatically detect hope speech. This paper describes the approach of our team in the task of hope speech detection. We use the attention mechanism to adjust the weight of all the output layers of XLM-RoBERTa to make full use of the information extracted from each layer, and use the weighted sum of all the output layers to complete the classification task. And we use the Stratified-K-Fold method to enhance the training data set. We achieve a weighted average F1-score of 0.59, 0.84, and 0.92 for Tamil, Malayalam, and English language, ranked 3rd, 2nd, and 2nd. | ['Xin Tao', 'Yingjia Zhao'] | null | null | null | null | eacl-ltedi-2021-4 | ['hope-speech-detection'] | ['natural-language-processing'] | [-1.79868624e-01 1.64969578e-01 -2.19510674e-01 -1.92130759e-01
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66cded31-8691-4a9f-8c56-256d10491836 | vietnamese-multi-document-summary-using | 2306.14827 | null | https://arxiv.org/abs/2306.14827v1 | https://arxiv.org/pdf/2306.14827v1.pdf | Vietnamese multi-document summary using subgraph selection approach -- VLSP 2022 AbMuSu Shared Task | Document summarization is a task to generate afluent, condensed summary for a document, andkeep important information. A cluster of documents serves as the input for multi-document summarizing (MDS), while the cluster summary serves as the output. In this paper, we focus on transforming the extractive MDS problem into subgraph selection. Approaching the problem in the form of graphs helps to capture simultaneously the relationship between sentences in the same document and between sentences in the same cluster based on exploiting the overall graph structure and selected subgraphs. Experiments have been implemented on the Vietnamese dataset published in VLSP Evaluation Campaign 2022. This model currently results in the top 10 participating teams reported on the ROUGH-2 $F\_1$ measure on the public test set. | ['Cam-Van Thi Nguyen', 'Tam Doan Thanh', 'Huu-Thin Nguyen'] | 2023-06-26 | null | null | null | null | ['document-summarization'] | ['natural-language-processing'] | [ 3.58461529e-01 6.13140345e-01 -1.39957100e-01 -2.07926989e-01
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e2fa2a54-6cde-4f1c-956d-91b6347f1693 | context-aware-chart-element-detection | 2305.04151 | null | https://arxiv.org/abs/2305.04151v1 | https://arxiv.org/pdf/2305.04151v1.pdf | Context-Aware Chart Element Detection | As a prerequisite of chart data extraction, the accurate detection of chart basic elements is essential and mandatory. In contrast to object detection in the general image domain, chart element detection relies heavily on context information as charts are highly structured data visualization formats. To address this, we propose a novel method CACHED, which stands for Context-Aware Chart Element Detection, by integrating a local-global context fusion module consisting of visual context enhancement and positional context encoding with the Cascade R-CNN framework. To improve the generalization of our method for broader applicability, we refine the existing chart element categorization and standardized 18 classes for chart basic elements, excluding plot elements. Our CACHED method, with the updated category of chart elements, achieves state-of-the-art performance in our experiments, underscoring the importance of context in chart element detection. Extending our method to the bar plot detection task, we obtain the best result on the PMC test dataset. | ['David Doermann', 'Saleem Ahmed', 'Pengyu Yan'] | 2023-05-07 | null | null | null | null | ['data-visualization', 'data-visualization', 'document-ai'] | ['methodology', 'miscellaneous', 'natural-language-processing'] | [ 3.52089971e-01 -4.40492719e-01 2.22519562e-02 -2.69850433e-01
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660cdc30-101e-4b14-a751-7e68042974c9 | content-based-models-of-quotation | null | null | https://aclanthology.org/2021.eacl-main.195 | https://aclanthology.org/2021.eacl-main.195.pdf | Content-based Models of Quotation | We explore the task of quotability identification, in which, given a document, we aim to identify which of its passages are the most quotable, i.e. the most likely to be directly quoted by later derived documents. We approach quotability identification as a passage ranking problem and evaluate how well both feature-based and BERT-based (Devlin et al., 2019) models rank the passages in a given document by their predicted quotability. We explore this problem through evaluations on five datasets that span multiple languages (English, Latin) and genres of literature (e.g. poetry, plays, novels) and whose corresponding derived documents are of multiple types (news, journal articles). Our experiments confirm the relatively strong performance of BERT-based models on this task, with the best model, a RoBERTA sequential sentence tagger, achieving an average rho of 0.35 and NDCG@1, 5, 50 of 0.26, 0.31 and 0.40, respectively, across all five datasets. | ['David Smith', 'Ansel MacLaughlin'] | 2021-04-01 | null | null | null | eacl-2021-2 | ['passage-ranking'] | ['natural-language-processing'] | [-2.23554969e-01 -2.63608247e-01 -4.74579483e-01 5.08320145e-02
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896ef78d-1b32-4e68-92bd-2f49e084e656 | dorsal-diffusion-for-object-centric | 2306.08068 | null | https://arxiv.org/abs/2306.08068v1 | https://arxiv.org/pdf/2306.08068v1.pdf | DORSal: Diffusion for Object-centric Representations of Scenes $\textit{et al.}$ | Recent progress in 3D scene understanding enables scalable learning of representations across large datasets of diverse scenes. As a consequence, generalization to unseen scenes and objects, rendering novel views from just a single or a handful of input images, and controllable scene generation that supports editing, is now possible. However, training jointly on a large number of scenes typically compromises rendering quality when compared to single-scene optimized models such as NeRFs. In this paper, we leverage recent progress in diffusion models to equip 3D scene representation learning models with the ability to render high-fidelity novel views, while retaining benefits such as object-level scene editing to a large degree. In particular, we propose DORSal, which adapts a video diffusion architecture for 3D scene generation conditioned on object-centric slot-based representations of scenes. On both complex synthetic multi-object scenes and on the real-world large-scale Street View dataset, we show that DORSal enables scalable neural rendering of 3D scenes with object-level editing and improves upon existing approaches. | ['Thomas Kipf', 'Mehdi S. M. Sajjadi', 'Emiel Hoogeboom', 'Sjoerd van Steenkiste', 'Allan Jabri'] | 2023-06-13 | null | null | null | null | ['neural-rendering', 'scene-generation', 'scene-understanding'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 5.73324203e-01 7.58767053e-02 3.11948121e-01 -4.02784079e-01
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79fd80c1-4536-487d-af81-2ba62e373bb7 | boost-ctr-prediction-for-new-advertisements | 2209.11727 | null | https://arxiv.org/abs/2209.11727v1 | https://arxiv.org/pdf/2209.11727v1.pdf | Boost CTR Prediction for New Advertisements via Modeling Visual Content | Existing advertisements click-through rate (CTR) prediction models are mainly dependent on behavior ID features, which are learned based on the historical user-ad interactions. Nevertheless, behavior ID features relying on historical user behaviors are not feasible to describe new ads without previous interactions with users. To overcome the limitations of behavior ID features in modeling new ads, we exploit the visual content in ads to boost the performance of CTR prediction models. Specifically, we map each ad into a set of visual IDs based on its visual content. These visual IDs are further used for generating the visual embedding for enhancing CTR prediction models. We formulate the learning of visual IDs into a supervised quantization problem. Due to a lack of class labels for commercial images in advertisements, we exploit image textual descriptions as the supervision to optimize the image extractor for generating effective visual IDs. Meanwhile, since the hard quantization is non-differentiable, we soften the quantization operation to make it support the end-to-end network training. After mapping each image into visual IDs, we learn the embedding for each visual ID based on the historical user-ad interactions accumulated in the past. Since the visual ID embedding depends only on the visual content, it generalizes well to new ads. Meanwhile, the visual ID embedding complements the ad behavior ID embedding. Thus, it can considerably boost the performance of the CTR prediction models previously relying on behavior ID features for both new ads and ads that have accumulated rich user behaviors. After incorporating the visual ID embedding in the CTR prediction model of Baidu online advertising, the average CTR of ads improves by 1.46%, and the total charge increases by 1.10%. | ['Ping Li', 'Hongliang Fei', 'Yi Yang', 'Jie Liu', 'Zhipeng Jin', 'Tan Yu'] | 2022-09-23 | null | null | null | null | ['click-through-rate-prediction'] | ['miscellaneous'] | [-1.15236349e-01 -1.70129776e-01 -6.14252031e-01 -6.47924662e-01
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c2fa7621-e8c1-428f-bd62-b65aa64c5f8b | human-trajectory-forecasting-with-explainable | 2307.01817 | null | https://arxiv.org/abs/2307.01817v1 | https://arxiv.org/pdf/2307.01817v1.pdf | Human Trajectory Forecasting with Explainable Behavioral Uncertainty | Human trajectory forecasting helps to understand and predict human behaviors, enabling applications from social robots to self-driving cars, and therefore has been heavily investigated. Most existing methods can be divided into model-free and model-based methods. Model-free methods offer superior prediction accuracy but lack explainability, while model-based methods provide explainability but cannot predict well. Combining both methodologies, we propose a new Bayesian Neural Stochastic Differential Equation model BNSP-SFM, where a behavior SDE model is combined with Bayesian neural networks (BNNs). While the NNs provide superior predictive power, the SDE offers strong explainability with quantifiable uncertainty in behavior and observation. We show that BNSP-SFM achieves up to a 50% improvement in prediction accuracy, compared with 11 state-of-the-art methods. BNSP-SFM also generalizes better to drastically different scenes with different environments and crowd densities (~ 20 times higher than the testing data). Finally, BNSP-SFM can provide predictions with confidence to better explain potential causes of behaviors. The code will be released upon acceptance. | ['He Wang', 'Dinesh Manocha', 'Jiangbei Yue'] | 2023-07-04 | null | null | null | null | ['self-driving-cars', 'trajectory-forecasting'] | ['computer-vision', 'computer-vision'] | [-1.15266152e-01 2.37757474e-01 -2.41296053e-01 -7.37232327e-01
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fcd59f58-f68c-4348-a8c3-c96127c2503e | semi-supervised-medical-image-classification-1 | 2005.11217 | null | https://arxiv.org/abs/2005.11217v1 | https://arxiv.org/pdf/2005.11217v1.pdf | Semi-supervised Medical Image Classification with Global Latent Mixing | Computer-aided diagnosis via deep learning relies on large-scale annotated data sets, which can be costly when involving expert knowledge. Semi-supervised learning (SSL) mitigates this challenge by leveraging unlabeled data. One effective SSL approach is to regularize the local smoothness of neural functions via perturbations around single data points. In this work, we argue that regularizing the global smoothness of neural functions by filling the void in between data points can further improve SSL. We present a novel SSL approach that trains the neural network on linear mixing of labeled and unlabeled data, at both the input and latent space in order to regularize different portions of the network. We evaluated the presented model on two distinct medical image data sets for semi-supervised classification of thoracic disease and skin lesion, demonstrating its improved performance over SSL with local perturbations and SSL with global mixing but at the input space only. Our code is available at https://github.com/Prasanna1991/LatentMixing. | ['Linwei Wang', 'Sandesh Ghimire', 'Zhiyuan Li', 'Prashnna Kumar Gyawali', 'Pradeep Bajracharya'] | 2020-05-22 | null | null | null | null | ['semi-supervised-medical-image-classification'] | ['medical'] | [ 3.26054275e-01 6.75788283e-01 -3.14616144e-01 -5.66386759e-01
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6619d74b-27ed-4d79-ae20-e3ac95c3f66f | clip-pae-projection-augmentation-embedding-to | 2210.03919 | null | https://arxiv.org/abs/2210.03919v4 | https://arxiv.org/pdf/2210.03919v4.pdf | CLIP-PAE: Projection-Augmentation Embedding to Extract Relevant Features for a Disentangled, Interpretable, and Controllable Text-Guided Face Manipulation | Recently introduced Contrastive Language-Image Pre-Training (CLIP) bridges images and text by embedding them into a joint latent space. This opens the door to ample literature that aims to manipulate an input image by providing a textual explanation. However, due to the discrepancy between image and text embeddings in the joint space, using text embeddings as the optimization target often introduces undesired artifacts in the resulting images. Disentanglement, interpretability, and controllability are also hard to guarantee for manipulation. To alleviate these problems, we propose to define corpus subspaces spanned by relevant prompts to capture specific image characteristics. We introduce CLIP Projection-Augmentation Embedding (PAE) as an optimization target to improve the performance of text-guided image manipulation. Our method is a simple and general paradigm that can be easily computed and adapted, and smoothly incorporated into any CLIP-based image manipulation algorithm. To demonstrate the effectiveness of our method, we conduct several theoretical and empirical studies. As a case study, we utilize the method for text-guided semantic face editing. We quantitatively and qualitatively demonstrate that PAE facilitates a more disentangled, interpretable, and controllable image manipulation with state-of-the-art quality and accuracy. | ['Fangcheng Zhong', 'Cengiz Oztireli', 'Chenliang Zhou'] | 2022-10-08 | null | null | null | null | ['image-manipulation'] | ['computer-vision'] | [ 6.60434186e-01 1.32625312e-01 -1.72310874e-01 -3.38590771e-01
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23b12b20-eae1-4c79-b306-ae0535d273c0 | deep-spectral-clustering-using-dual | 1904.13113 | null | http://arxiv.org/abs/1904.13113v1 | http://arxiv.org/pdf/1904.13113v1.pdf | Deep Spectral Clustering using Dual Autoencoder Network | The clustering methods have recently absorbed even-increasing attention in
learning and vision. Deep clustering combines embedding and clustering together
to obtain optimal embedding subspace for clustering, which can be more
effective compared with conventional clustering methods. In this paper, we
propose a joint learning framework for discriminative embedding and spectral
clustering. We first devise a dual autoencoder network, which enforces the
reconstruction constraint for the latent representations and their noisy
versions, to embed the inputs into a latent space for clustering. As such the
learned latent representations can be more robust to noise. Then the mutual
information estimation is utilized to provide more discriminative information
from the inputs. Furthermore, a deep spectral clustering method is applied to
embed the latent representations into the eigenspace and subsequently clusters
them, which can fully exploit the relationship between inputs to achieve
optimal clustering results. Experimental results on benchmark datasets show
that our method can significantly outperform state-of-the-art clustering
approaches. | ['Feng Zheng', 'Wei Liu', 'Junchi Yan', 'Xu Yang', 'Cheng Deng'] | 2019-04-30 | deep-spectral-clustering-using-dual-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Yang_Deep_Spectral_Clustering_Using_Dual_Autoencoder_Network_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Yang_Deep_Spectral_Clustering_Using_Dual_Autoencoder_Network_CVPR_2019_paper.pdf | cvpr-2019-6 | ['mutual-information-estimation'] | ['methodology'] | [-4.55867857e-01 -4.93847251e-01 -8.09977427e-02 -3.49728942e-01
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6b222717-6128-481a-a3cd-97cab517a349 | can-humans-fly-action-understanding-with | null | null | http://openaccess.thecvf.com/content_cvpr_2015/html/Xu_Can_Humans_Fly_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Xu_Can_Humans_Fly_2015_CVPR_paper.pdf | Can Humans Fly? Action Understanding With Multiple Classes of Actors | Can humans fly? Emphatically no. Can cars eat? Again, absolutely not. Yet, these absurd inferences result from the current disregard for particular types of actors in action understanding. There is no work we know of on simultaneously inferring actors and actions in the video, not to mention a dataset to experiment with. Our paper hence marks the first effort in the computer vision community to jointly consider various types of actors undergoing various actions. To start with the problem, we collect a dataset of 3782 videos from YouTube and label both pixel-level actors and actions in each video. We formulate the general actor-action understanding problem and instantiate it at various granularities: both video-level single- and multiple-label actor-action recognition and pixel-level actor-action semantic segmentation. Our experiments demonstrate that inference jointly over actors and actions outperforms inference independently over them, and hence concludes our argument of the value of explicit consideration of various actors in comprehensive action understanding. | ['Jason J. Corso', 'Shao-Hang Hsieh', 'Chenliang Xu', 'Caiming Xiong'] | 2015-06-01 | null | null | null | cvpr-2015-6 | ['action-understanding'] | ['computer-vision'] | [ 7.84174562e-01 3.27514738e-01 -6.03154242e-01 -6.54419422e-01
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9d48fcd7-2ada-44f3-be03-d5f73dcda16c | contextually-enhanced-es-drnn-with-dynamic | 2212.09030 | null | https://arxiv.org/abs/2212.09030v1 | https://arxiv.org/pdf/2212.09030v1.pdf | Contextually Enhanced ES-dRNN with Dynamic Attention for Short-Term Load Forecasting | In this paper, we propose a new short-term load forecasting (STLF) model based on contextually enhanced hybrid and hierarchical architecture combining exponential smoothing (ES) and a recurrent neural network (RNN). The model is composed of two simultaneously trained tracks: the context track and the main track. The context track introduces additional information to the main track. It is extracted from representative series and dynamically modulated to adjust to the individual series forecasted by the main track. The RNN architecture consists of multiple recurrent layers stacked with hierarchical dilations and equipped with recently proposed attentive dilated recurrent cells. These cells enable the model to capture short-term, long-term and seasonal dependencies across time series as well as to weight dynamically the input information. The model produces both point forecasts and predictive intervals. The experimental part of the work performed on 35 forecasting problems shows that the proposed model outperforms in terms of accuracy its predecessor as well as standard statistical models and state-of-the-art machine learning models. | ['Paweł Pełka', 'Grzegorz Dudek', 'Slawek Smyl'] | 2022-12-18 | null | null | null | null | ['load-forecasting'] | ['miscellaneous'] | [ 1.28335819e-01 3.54694836e-02 3.51407006e-02 -4.65412378e-01
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f94bc2ae-5dad-4a92-a18f-9a0c0da5d866 | mobasa-corpus-for-aspect-based-sentiment | null | null | https://aclanthology.org/2022.csrnlp-1.5 | https://aclanthology.org/2022.csrnlp-1.5.pdf | MobASA: Corpus for Aspect-based Sentiment Analysis and Social Inclusion in the Mobility Domain | In this paper we show how aspect-based sentiment analysis might help public transport companies to improve their social responsibility for accessible travel. We present MobASA: a novel German-language corpus of tweets annotated with their relevance for public transportation, and with sentiment towards aspects related to barrier-free travel. We identified and labeled topics important for passengers limited in their mobility due to disability, age, or when travelling with young children. The data can be used to identify hurdles and improve travel planning for vulnerable passengers, as well as to monitor a perception of transportation businesses regarding the social inclusion of all passengers. The data is publicly available under: https://github.com/DFKI-NLP/sim3s-corpus | ['Philippe Thomas', 'Aleksandra Gabryszak'] | null | null | null | null | csrnlp-lrec-2022-6 | ['aspect-based-sentiment-analysis'] | ['natural-language-processing'] | [-3.61728460e-01 3.65598798e-01 -6.51942611e-01 -5.40194690e-01
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f3d6c375-da3b-4081-aaf4-94251c3ce1a7 | what-knowledge-is-needed-towards-explainable | 2211.04052 | null | https://arxiv.org/abs/2211.04052v2 | https://arxiv.org/pdf/2211.04052v2.pdf | What Knowledge Is Needed? Towards Explainable Memory for kNN-MT Domain Adaptation | kNN-MT presents a new paradigm for domain adaptation by building an external datastore, which usually saves all target language token occurrences in the parallel corpus. As a result, the constructed datastore is usually large and possibly redundant. In this paper, we investigate the interpretability issue of this approach: what knowledge does the NMT model need? We propose the notion of local correctness (LAC) as a new angle, which describes the potential translation correctness for a single entry and for a given neighborhood. Empirical study shows that our investigation successfully finds the conditions where the NMT model could easily fail and need related knowledge. Experiments on six diverse target domains and two language-pairs show that pruning according to local correctness brings a light and more explainable memory for kNN-MT domain adaptation. | ['Jiajun Chen', 'Xin Zheng', 'Yunzhe Lv', 'ShuJian Huang', 'Wenhao Zhu'] | 2022-11-08 | null | null | null | null | ['nmt'] | ['computer-code'] | [ 1.86057955e-01 2.29420111e-01 -5.39557993e-01 -4.13902372e-01
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ba4b975d-3121-480d-9826-d7f60e7d3f52 | using-adaptive-gradient-for-texture-learning | 2104.14169 | null | https://arxiv.org/abs/2104.14169v1 | https://arxiv.org/pdf/2104.14169v1.pdf | Using Adaptive Gradient for Texture Learning in Single-View 3D Reconstruction | Recently, learning-based approaches for 3D model reconstruction have attracted attention owing to its modern applications such as Extended Reality(XR), robotics and self-driving cars. Several approaches presented good performance on reconstructing 3D shapes by learning solely from images, i.e., without using 3D models in training. Challenges, however, remain in texture generation due to the gap between 2D and 3D modals. In previous work, the grid sampling mechanism from Spatial Transformer Networks was adopted to sample color from an input image to formulate texture. Despite its success, the existing framework has limitations on searching scope in sampling, resulting in flaws in generated texture and consequentially on rendered 3D models. In this paper, to solve that issue, we present a novel sampling algorithm by optimizing the gradient of predicted coordinates based on the variance on the sampling image. Taking into account the semantics of the image, we adopt Frechet Inception Distance (FID) to form a loss function in learning, which helps bridging the gap between rendered images and input images. As a result, we greatly improve generated texture. Furthermore, to optimize 3D shape reconstruction and to accelerate convergence at training, we adopt part segmentation and template learning in our model. Without any 3D supervision in learning, and with only a collection of single-view 2D images, the shape and texture learned by our model outperform those from previous work. We demonstrate the performance with experimental results on a publically available dataset. | ['Dihong Tian', 'Luoyang Lin'] | 2021-04-29 | null | null | null | null | ['texture-synthesis', 'single-view-3d-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 1.53409109e-01 1.13843761e-01 -2.61904933e-02 -2.97829598e-01
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5f55ffd6-76df-49f2-a02d-440763899569 | deepstruct-pretraining-of-language-models-for-1 | 2205.10475 | null | https://arxiv.org/abs/2205.10475v2 | https://arxiv.org/pdf/2205.10475v2.pdf | DeepStruct: Pretraining of Language Models for Structure Prediction | We introduce a method for improving the structural understanding abilities of language models. Unlike previous approaches that finetune the models with task-specific augmentation, we pretrain language models on a collection of task-agnostic corpora to generate structures from text. Our structure pretraining enables zero-shot transfer of the learned knowledge that models have about the structure tasks. We study the performance of this approach on 28 datasets, spanning 10 structure prediction tasks including open information extraction, joint entity and relation extraction, named entity recognition, relation classification, semantic role labeling, event extraction, coreference resolution, factual probe, intent detection, and dialogue state tracking. We further enhance the pretraining with the task-specific training sets. We show that a 10B parameter language model transfers non-trivially to most tasks and obtains state-of-the-art performance on 21 of 28 datasets that we evaluate. | ['Dawn Song', 'Jie Tang', 'Haoyun Hong', 'Zui Chen', 'Xiao Liu', 'Chenguang Wang'] | 2022-05-21 | deepstruct-pretraining-of-language-models-for | https://aclanthology.org/2022.findings-acl.67 | https://aclanthology.org/2022.findings-acl.67.pdf | findings-acl-2022-5 | ['open-information-extraction', 'dialogue-state-tracking', 'semantic-role-labeling', 'relation-classification', 'joint-entity-and-relation-extraction'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 4.91098821e-01 1.10269701e+00 -4.52351511e-01 -6.40811145e-01
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87b94dc6-025d-441e-a78b-69443ff00f4c | learning-to-discover-reflection-symmetry-via | 2108.12952 | null | https://arxiv.org/abs/2108.12952v2 | https://arxiv.org/pdf/2108.12952v2.pdf | Learning to Discover Reflection Symmetry via Polar Matching Convolution | The task of reflection symmetry detection remains challenging due to significant variations and ambiguities of symmetry patterns in the wild. Furthermore, since the local regions are required to match in reflection for detecting a symmetry pattern, it is hard for standard convolutional networks, which are not equivariant to rotation and reflection, to learn the task. To address the issue, we introduce a new convolutional technique, dubbed the polar matching convolution, which leverages a polar feature pooling, a self-similarity encoding, and a systematic kernel design for axes of different angles. The proposed high-dimensional kernel convolution network effectively learns to discover symmetry patterns from real-world images, overcoming the limitations of standard convolution. In addition, we present a new dataset and introduce a self-supervised learning strategy by augmenting the dataset with synthesizing images. Experiments demonstrate that our method outperforms state-of-the-art methods in terms of accuracy and robustness. | ['Minsu Cho', 'Woohyeon Shim', 'Ahyun Seo'] | 2021-08-30 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Seo_Learning_To_Discover_Reflection_Symmetry_via_Polar_Matching_Convolution_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Seo_Learning_To_Discover_Reflection_Symmetry_via_Polar_Matching_Convolution_ICCV_2021_paper.pdf | iccv-2021-1 | ['symmetry-detection'] | ['computer-vision'] | [ 3.49642634e-01 -1.64282814e-01 1.14455596e-01 -4.78858083e-01
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4b347892-0f8a-4cd8-a822-fdfed883e6d2 | direction-aware-joint-adaptation-of-neural | 2207.07273 | null | https://arxiv.org/abs/2207.07273v1 | https://arxiv.org/pdf/2207.07273v1.pdf | Direction-Aware Joint Adaptation of Neural Speech Enhancement and Recognition in Real Multiparty Conversational Environments | This paper describes noisy speech recognition for an augmented reality headset that helps verbal communication within real multiparty conversational environments. A major approach that has actively been studied in simulated environments is to sequentially perform speech enhancement and automatic speech recognition (ASR) based on deep neural networks (DNNs) trained in a supervised manner. In our task, however, such a pretrained system fails to work due to the mismatch between the training and test conditions and the head movements of the user. To enhance only the utterances of a target speaker, we use beamforming based on a DNN-based speech mask estimator that can adaptively extract the speech components corresponding to a head-relative particular direction. We propose a semi-supervised adaptation method that jointly updates the mask estimator and the ASR model at run-time using clean speech signals with ground-truth transcriptions and noisy speech signals with highly-confident estimated transcriptions. Comparative experiments using the state-of-the-art distant speech recognition system show that the proposed method significantly improves the ASR performance. | ['Kazuyoshi Yoshii', 'Mathieu Fontaine', 'Yoshiaki Bando', 'Kouhei Sekiguchi', 'Aditya Arie Nugraha', 'Yicheng Du'] | 2022-07-15 | null | null | null | null | ['noisy-speech-recognition', 'distant-speech-recognition'] | ['speech', 'speech'] | [ 3.52369279e-01 3.55831921e-01 6.15370810e-01 -7.45052934e-01
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c9e1868b-ad5c-4ef6-b840-2db750634c1b | improving-low-resource-cross-lingual-parsing | 2210.09428 | null | https://arxiv.org/abs/2210.09428v1 | https://arxiv.org/pdf/2210.09428v1.pdf | Improving Low-Resource Cross-lingual Parsing with Expected Statistic Regularization | We present Expected Statistic Regularization (ESR), a novel regularization technique that utilizes low-order multi-task structural statistics to shape model distributions for semi-supervised learning on low-resource datasets. We study ESR in the context of cross-lingual transfer for syntactic analysis (POS tagging and labeled dependency parsing) and present several classes of low-order statistic functions that bear on model behavior. Experimentally, we evaluate the proposed statistics with ESR for unsupervised transfer on 5 diverse target languages and show that all statistics, when estimated accurately, yield improvements to both POS and LAS, with the best statistic improving POS by +7.0 and LAS by +8.5 on average. We also present semi-supervised transfer and learning curve experiments that show ESR provides significant gains over strong cross-lingual-transfer-plus-fine-tuning baselines for modest amounts of label data. These results indicate that ESR is a promising and complementary approach to model-transfer approaches for cross-lingual parsing. | ['Michael Collins', 'Thomas Effland'] | 2022-10-17 | null | null | null | null | ['dependency-parsing'] | ['natural-language-processing'] | [-8.67264438e-03 2.74554610e-01 -4.79819030e-01 -9.67422962e-01
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2353862c-0032-4e4e-b21f-e4f4ebbcc593 | amd-hooknet-for-glacier-front-segmentation | 2302.02744 | null | https://arxiv.org/abs/2302.02744v1 | https://arxiv.org/pdf/2302.02744v1.pdf | AMD-HookNet for Glacier Front Segmentation | Knowledge on changes in glacier calving front positions is important for assessing the status of glaciers. Remote sensing imagery provides the ideal database for monitoring calving front positions, however, it is not feasible to perform this task manually for all calving glaciers globally due to time-constraints. Deep learning-based methods have shown great potential for glacier calving front delineation from optical and radar satellite imagery. The calving front is represented as a single thin line between the ocean and the glacier, which makes the task vulnerable to inaccurate predictions. The limited availability of annotated glacier imagery leads to a lack of data diversity (not all possible combinations of different weather conditions, terminus shapes, sensors, etc. are present in the data), which exacerbates the difficulty of accurate segmentation. In this paper, we propose Attention-Multi-hooking-Deep-supervision HookNet (AMD-HookNet), a novel glacier calving front segmentation framework for synthetic aperture radar (SAR) images. The proposed method aims to enhance the feature representation capability through multiple information interactions between low-resolution and high-resolution inputs based on a two-branch U-Net. The attention mechanism, integrated into the two branch U-Net, aims to interact between the corresponding coarse and fine-grained feature maps. This allows the network to automatically adjust feature relationships, resulting in accurate pixel-classification predictions. Extensive experiments and comparisons on the challenging glacier segmentation benchmark dataset CaFFe show that our AMD-HookNet achieves a mean distance error of 438 m to the ground truth outperforming the current state of the art by 42%, which validates its effectiveness. | ['Vincent Christlein', 'Andreas Maier', 'Matthias Braun', 'Jianlin Zhang', 'Thorsten Seehaus', 'Nora Gourmelon', 'Fei Wu'] | 2023-02-06 | null | null | null | null | ['calving-front-delineation-in-synthetic'] | ['computer-vision'] | [ 2.96919376e-01 -1.98592111e-01 1.47255242e-01 -5.42565048e-01
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d27f6002-d2ba-42d3-8994-e519e4ac96e9 | scc-automatic-classification-of-code-snippets | 1809.07945 | null | http://arxiv.org/abs/1809.07945v1 | http://arxiv.org/pdf/1809.07945v1.pdf | SCC: Automatic Classification of Code Snippets | Determining the programming language of a source code file has been
considered in the research community; it has been shown that Machine Learning
(ML) and Natural Language Processing (NLP) algorithms can be effective in
identifying the programming language of source code files. However, determining
the programming language of a code snippet or a few lines of source code is
still a challenging task. Online forums such as Stack Overflow and code
repositories such as GitHub contain a large number of code snippets. In this
paper, we describe Source Code Classification (SCC), a classifier that can
identify the programming language of code snippets written in 21 different
programming languages. A Multinomial Naive Bayes (MNB) classifier is employed
which is trained using Stack Overflow posts. It is shown to achieve an accuracy
of 75% which is higher than that with Programming Languages Identification (PLI
a proprietary online classifier of snippets) whose accuracy is only 55.5%. The
average score for precision, recall and the F1 score with the proposed tool are
0.76, 0.75 and 0.75, respectively. In addition, it can distinguish between code
snippets from a family of programming languages such as C, C++ and C#, and can
also identify the programming language version such as C# 3.0, C# 4.0 and C#
5.0. | ['Daniel M. German', 'Venkatesh Srinivasan', 'T. Aaron Gulliver', 'Kamel Alreshedy', 'Dhanush Dharmaretnam'] | 2018-09-21 | null | null | null | null | ['code-classification'] | ['computer-code'] | [-3.88741761e-01 -2.24189475e-01 -5.50845563e-01 -5.91059364e-02
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396cab59-be5e-4b9a-8660-e75c45222a4f | pointr-diverse-point-cloud-completion-with | 2108.08839 | null | https://arxiv.org/abs/2108.08839v1 | https://arxiv.org/pdf/2108.08839v1.pdf | PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformers | Point clouds captured in real-world applications are often incomplete due to the limited sensor resolution, single viewpoint, and occlusion. Therefore, recovering the complete point clouds from partial ones becomes an indispensable task in many practical applications. In this paper, we present a new method that reformulates point cloud completion as a set-to-set translation problem and design a new model, called PoinTr that adopts a transformer encoder-decoder architecture for point cloud completion. By representing the point cloud as a set of unordered groups of points with position embeddings, we convert the point cloud to a sequence of point proxies and employ the transformers for point cloud generation. To facilitate transformers to better leverage the inductive bias about 3D geometric structures of point clouds, we further devise a geometry-aware block that models the local geometric relationships explicitly. The migration of transformers enables our model to better learn structural knowledge and preserve detailed information for point cloud completion. Furthermore, we propose two more challenging benchmarks with more diverse incomplete point clouds that can better reflect the real-world scenarios to promote future research. Experimental results show that our method outperforms state-of-the-art methods by a large margin on both the new benchmarks and the existing ones. Code is available at https://github.com/yuxumin/PoinTr | ['Jie zhou', 'Jiwen Lu', 'Zuyan Liu', 'Ziyi Wang', 'Yongming Rao', 'Xumin Yu'] | 2021-08-19 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Yu_PoinTr_Diverse_Point_Cloud_Completion_With_Geometry-Aware_Transformers_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Yu_PoinTr_Diverse_Point_Cloud_Completion_With_Geometry-Aware_Transformers_ICCV_2021_paper.pdf | iccv-2021-1 | ['point-cloud-completion', 'point-cloud-generation'] | ['computer-vision', 'computer-vision'] | [-1.08750746e-01 -2.64141738e-01 -1.76474094e-01 -4.61403579e-01
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17cb98d8-9dff-431a-9ab2-b2e6e1fd7258 | towards-holistic-scene-understanding-semantic | 2201.07734 | null | https://arxiv.org/abs/2201.07734v1 | https://arxiv.org/pdf/2201.07734v1.pdf | Towards holistic scene understanding: Semantic segmentation and beyond | This dissertation addresses visual scene understanding and enhances segmentation performance and generalization, training efficiency of networks, and holistic understanding. First, we investigate semantic segmentation in the context of street scenes and train semantic segmentation networks on combinations of various datasets. In Chapter 2 we design a framework of hierarchical classifiers over a single convolutional backbone, and train it end-to-end on a combination of pixel-labeled datasets, improving generalizability and the number of recognizable semantic concepts. Chapter 3 focuses on enriching semantic segmentation with weak supervision and proposes a weakly-supervised algorithm for training with bounding box-level and image-level supervision instead of only with per-pixel supervision. The memory and computational load challenges that arise from simultaneous training on multiple datasets are addressed in Chapter 4. We propose two methodologies for selecting informative and diverse samples from datasets with weak supervision to reduce our networks' ecological footprint without sacrificing performance. Motivated by memory and computation efficiency requirements, in Chapter 5, we rethink simultaneous training on heterogeneous datasets and propose a universal semantic segmentation framework. This framework achieves consistent increases in performance metrics and semantic knowledgeability by exploiting various scene understanding datasets. Chapter 6 introduces the novel task of part-aware panoptic segmentation, which extends our reasoning towards holistic scene understanding. This task combines scene and parts-level semantics with instance-level object detection. In conclusion, our contributions span over convolutional network architectures, weakly-supervised learning, part and panoptic segmentation, paving the way towards a holistic, rich, and sustainable visual scene understanding. | ['Panagiotis Meletis'] | 2022-01-16 | null | null | null | null | ['part-level-panoptic-segmentation'] | ['computer-vision'] | [ 6.93879604e-01 1.14430062e-01 -5.84747732e-01 -6.62543952e-01
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cb06d5c7-ca86-478f-931c-24c8c74bdaf9 | high-sensitivity-electric-potential-sensors | 2110.12313 | null | https://arxiv.org/abs/2110.12313v1 | https://arxiv.org/pdf/2110.12313v1.pdf | High-Sensitivity Electric Potential Sensors for Non-Contact Monitoring of Physiological Signals | The paper describes highly-sensitive passive electric potential sensors (EPS) for non-contact detection of multiple biophysical signals, including electrocardiogram (ECG), respiration cycle (RC), and electroencephalogram (EEG). The proposed EPS uses an optimized transimpedance amplifier (TIA), a single guarded sensing electrode, and an adaptive cancellation loop (ACL) to maximize sensitivity (DC transimpedance $=150$~G$\Omega$) in the presence of power line interference (PLI) and motion artifacts. Tests were performed on healthy adult volunteers in noisy and unshielded indoor environments. Useful sensing ranges for ECG, RC, and EEG measurements, as validated against reference contact sensors, were observed to be approximately 50~cm, 100~cm, and 5~cm, respectively. ECG and RC signals were also successfully measured through wooden tables for subjects in sleep-like postures. The EPS were integrated with a wireless microcontroller to realize wireless sensor nodes capable of streaming acquired data to a remote base station in real-time. | ['Tayfun Ozdemir', 'Kevin Bi', 'Soumyajit Mandal', 'Wangbo Chen', 'Xinyao Tang'] | 2021-10-23 | null | null | null | null | ['contact-detection'] | ['robots'] | [ 4.58876401e-01 -2.15659976e-01 6.54647708e-01 -1.67944357e-01
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85045de7-7b73-4134-aba9-2746371e96db | paraphrase-generation-a-survey-of-the-state | null | null | https://aclanthology.org/2021.emnlp-main.414 | https://aclanthology.org/2021.emnlp-main.414.pdf | Paraphrase Generation: A Survey of the State of the Art | This paper focuses on paraphrase generation,which is a widely studied natural language generation task in NLP. With the development of neural models, paraphrase generation research has exhibited a gradual shift to neural methods in the recent years. This has provided architectures for contextualized representation of an input text and generating fluent, diverseand human-like paraphrases. This paper surveys various approaches to paraphrase generation with a main focus on neural methods. | ['Suma Bhat', 'Jianing Zhou'] | null | null | null | null | emnlp-2021-11 | ['paraphrase-generation', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing'] | [ 3.96626621e-01 4.03930843e-01 -3.32355261e-01 -3.68865222e-01
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d874e994-ff1d-48eb-9b30-fc15e0c3daff | implicit-sample-extension-for-unsupervised | 2204.06892 | null | https://arxiv.org/abs/2204.06892v1 | https://arxiv.org/pdf/2204.06892v1.pdf | Implicit Sample Extension for Unsupervised Person Re-Identification | Most existing unsupervised person re-identification (Re-ID) methods use clustering to generate pseudo labels for model training. Unfortunately, clustering sometimes mixes different true identities together or splits the same identity into two or more sub clusters. Training on these noisy clusters substantially hampers the Re-ID accuracy. Due to the limited samples in each identity, we suppose there may lack some underlying information to well reveal the accurate clusters. To discover these information, we propose an Implicit Sample Extension (\OurWholeMethod) method to generate what we call support samples around the cluster boundaries. Specifically, we generate support samples from actual samples and their neighbouring clusters in the embedding space through a progressive linear interpolation (PLI) strategy. PLI controls the generation with two critical factors, i.e., 1) the direction from the actual sample towards its K-nearest clusters and 2) the degree for mixing up the context information from the K-nearest clusters. Meanwhile, given the support samples, ISE further uses a label-preserving loss to pull them towards their corresponding actual samples, so as to compact each cluster. Consequently, ISE reduces the "sub and mixed" clustering errors, thus improving the Re-ID performance. Extensive experiments demonstrate that the proposed method is effective and achieves state-of-the-art performance for unsupervised person Re-ID. Code is available at: \url{https://github.com/PaddlePaddle/PaddleClas}. | ['Jingdong Wang', 'Zhaoxiang Zhang', 'Javen Qinfeng Shi', 'Errui Ding', 'Jian Wang', 'Zhigang Wang', 'Dongdong Li', 'Xinyu Zhang'] | 2022-04-14 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Zhang_Implicit_Sample_Extension_for_Unsupervised_Person_Re-Identification_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Zhang_Implicit_Sample_Extension_for_Unsupervised_Person_Re-Identification_CVPR_2022_paper.pdf | cvpr-2022-1 | ['unsupervised-person-re-identification'] | ['computer-vision'] | [-1.47947356e-01 -1.01908475e-01 -3.10685158e-01 -3.97821844e-01
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956cfa72-89cf-4609-9689-654a52e3b4e6 | online-knowledge-distillation-via-mutual | 2207.11518 | null | https://arxiv.org/abs/2207.11518v2 | https://arxiv.org/pdf/2207.11518v2.pdf | Online Knowledge Distillation via Mutual Contrastive Learning for Visual Recognition | The teacher-free online Knowledge Distillation (KD) aims to train an ensemble of multiple student models collaboratively and distill knowledge from each other. Although existing online KD methods achieve desirable performance, they often focus on class probabilities as the core knowledge type, ignoring the valuable feature representational information. We present a Mutual Contrastive Learning (MCL) framework for online KD. The core idea of MCL is to perform mutual interaction and transfer of contrastive distributions among a cohort of networks in an online manner. Our MCL can aggregate cross-network embedding information and maximize the lower bound to the mutual information between two networks. This enables each network to learn extra contrastive knowledge from others, leading to better feature representations, thus improving the performance of visual recognition tasks. Beyond the final layer, we extend MCL to intermediate layers and perform an adaptive layer-matching mechanism trained by meta-optimization. Experiments on image classification and transfer learning to visual recognition tasks show that layer-wise MCL can lead to consistent performance gains against state-of-the-art online KD approaches. The superiority demonstrates that layer-wise MCL can guide the network to generate better feature representations. Our code is publicly avaliable at https://github.com/winycg/L-MCL. | ['Yongjun Xu', 'Fuzhen Zhuang', 'Qian Zhan', 'Helong Zhou', 'Zhulin An', 'Chuanguang Yang'] | 2022-07-23 | null | null | null | null | ['network-embedding'] | ['methodology'] | [-3.97653162e-01 4.79928292e-02 -2.95203984e-01 -5.08371413e-01
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04ea7318-8a94-4ad1-b5e9-a9154990f298 | dynamic-collaborative-filtering-thompson | 2208.11926 | null | https://arxiv.org/abs/2208.11926v2 | https://arxiv.org/pdf/2208.11926v2.pdf | Dynamic collaborative filtering Thompson Sampling for cross-domain advertisements recommendation | Recently online advertisers utilize Recommender systems (RSs) for display advertising to improve users' engagement. The contextual bandit model is a widely used RS to exploit and explore users' engagement and maximize the long-term rewards such as clicks or conversions. However, the current models aim to optimize a set of ads only in a specific domain and do not share information with other models in multiple domains. In this paper, we propose dynamic collaborative filtering Thompson Sampling (DCTS), the novel yet simple model to transfer knowledge among multiple bandit models. DCTS exploits similarities between users and between ads to estimate a prior distribution of Thompson sampling. Such similarities are obtained based on contextual features of users and ads. Similarities enable models in a domain that didn't have much data to converge more quickly by transferring knowledge. Moreover, DCTS incorporates temporal dynamics of users to track the user's recent change of preference. We first show transferring knowledge and incorporating temporal dynamics improve the performance of the baseline models on a synthetic dataset. Then we conduct an empirical analysis on a real-world dataset and the result showed that DCTS improves click-through rate by 9.7% than the state-of-the-art models. We also analyze hyper-parameters that adjust temporal dynamics and similarities and show the best parameter which maximizes CTR. | ['Yu Hirate', 'Young-joo Chung', 'Shion Ishikawa'] | 2022-08-25 | null | null | null | null | ['thompson-sampling'] | ['methodology'] | [-3.19296449e-01 -3.86586457e-01 -1.01452339e+00 -7.47470319e-01
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0a081487-06a9-48a5-a2df-38232e15c93f | an-unsupervised-approach-to-discover-media | null | null | https://aclanthology.org/2022.politicalnlp-1.4 | https://aclanthology.org/2022.politicalnlp-1.4.pdf | An Unsupervised Approach to Discover Media Frames | Media framing refers to highlighting certain aspect of an issue in the news to promote a particular interpretation to the audience. Supervised learning has often been used to recognize frames in news articles, requiring a known pool of frames for a particular issue, which must be identified by communication researchers through thorough manual content analysis. In this work, we devise an unsupervised learning approach to discover the frames in news articles automatically. Given a set of news articles for a given issue, e.g., gun violence, our method first extracts frame elements from these articles using related Wikipedia articles and the Wikipedia category system. It then uses a community detection approach to identify frames from these frame elements. We discuss the effectiveness of our approach by comparing the frames it generates in an unsupervised manner to the domain-expert-derived frames for the issue of gun violence, for which a supervised learning model for frame recognition exists. | ['Derry Tanti Wijaya', 'Prakash Ishwar', 'Margrit Betke', 'Lei Guo', 'Yanru Jiang', 'Sha Lai'] | null | null | null | null | politicalnlp-lrec-2022-6 | ['community-detection'] | ['graphs'] | [ 6.22516930e-01 5.28092742e-01 -5.93992472e-01 -1.56701252e-01
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60fc0030-621a-4560-a8af-5cbb5ab957d3 | online-multi-target-regression-trees-with | 1903.12483 | null | https://arxiv.org/abs/1903.12483v4 | https://arxiv.org/pdf/1903.12483v4.pdf | Online Multi-target regression trees with stacked leaf models | One of the current challenges in machine learning is how to deal with data coming at increasing rates in data streams. New predictive learning strategies are needed to cope with the high throughput data and concept drift. One of the data stream mining tasks where new learning strategies are needed is multi-target regression, due to its applicability in a high number of real world problems. While reliable and effective learning strategies have been proposed for batch multi-target regression, few have been proposed for multi-target online learning in data streams. Besides, most of the existing solutions do not consider the occurrence of inter-target correlations when making predictions. In this work, we propose a novel online learning strategy for multi-target regression in data streams. The proposed strategy extends existing online decision tree learning algorithm to explore inter-target dependencies while making predictions. For such, the proposed strategy, called Stacked Single-target Hoeffding Tree (SST-HT), uses the inter-target dependencies as an additional information source to enhance predictive accuracy. Throughout an extensive experimental setup, we evaluate our proposal against state-of-the-art decision tree-based algorithms for online multi-target regression. According to the experimental results, SST-HT presents superior predictive accuracy, with a small increase in the processing time and memory requirements. | ['André Carlos Ponce de Leon Ferreira de Carvalho', 'Sylvio Barbon Jr.', 'Saulo Martiello Mastelini'] | 2019-03-29 | null | null | null | null | ['multi-target-regression'] | ['miscellaneous'] | [ 3.91719609e-01 -5.11638224e-01 -6.34838045e-01 -3.63342732e-01
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82478890-8e6f-4659-988f-20b5e9529e44 | wavenet-a-generative-model-for-raw-audio | 1609.03499 | null | http://arxiv.org/abs/1609.03499v2 | http://arxiv.org/pdf/1609.03499v2.pdf | WaveNet: A Generative Model for Raw Audio | This paper introduces WaveNet, a deep neural network for generating raw audio
waveforms. The model is fully probabilistic and autoregressive, with the
predictive distribution for each audio sample conditioned on all previous ones;
nonetheless we show that it can be efficiently trained on data with tens of
thousands of samples per second of audio. When applied to text-to-speech, it
yields state-of-the-art performance, with human listeners rating it as
significantly more natural sounding than the best parametric and concatenative
systems for both English and Mandarin. A single WaveNet can capture the
characteristics of many different speakers with equal fidelity, and can switch
between them by conditioning on the speaker identity. When trained to model
music, we find that it generates novel and often highly realistic musical
fragments. We also show that it can be employed as a discriminative model,
returning promising results for phoneme recognition. | ['Sander Dieleman', 'Koray Kavukcuoglu', 'Nal Kalchbrenner', 'Karen Simonyan', 'Oriol Vinyals', 'Heiga Zen', 'Andrew Senior', 'Alex Graves', 'Aaron van den Oord'] | 2016-09-12 | null | null | null | null | ['audio-generation'] | ['audio'] | [ 2.66465575e-01 -4.31145765e-02 1.59772456e-01 -1.80019945e-01
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ac3e741e-9b37-463f-ae16-8f6051721ea1 | robust-lexicalized-native-language | null | null | https://aclanthology.org/C12-1025 | https://aclanthology.org/C12-1025.pdf | Robust, Lexicalized Native Language Identification | null | ['Julian Brooke', 'Graeme Hirst'] | 2012-12-01 | robust-lexicalized-native-language-1 | https://aclanthology.org/C12-1025 | https://aclanthology.org/C12-1025.pdf | coling-2012-12 | ['native-language-identification'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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63764976-cc2a-40cd-87c2-6d6e76121ed7 | learning-elementary-structures-for-3d-shape | 1908.04725 | null | https://arxiv.org/abs/1908.04725v2 | https://arxiv.org/pdf/1908.04725v2.pdf | Learning elementary structures for 3D shape generation and matching | We propose to represent shapes as the deformation and combination of learnable elementary 3D structures, which are primitives resulting from training over a collection of shape. We demonstrate that the learned elementary 3D structures lead to clear improvements in 3D shape generation and matching. More precisely, we present two complementary approaches for learning elementary structures: (i) patch deformation learning and (ii) point translation learning. Both approaches can be extended to abstract structures of higher dimensions for improved results. We evaluate our method on two tasks: reconstructing ShapeNet objects and estimating dense correspondences between human scans (FAUST inter challenge). We show 16% improvement over surface deformation approaches for shape reconstruction and outperform FAUST inter challenge state of the art by 6%. | ['Thibault Groueix', 'Theo Deprelle', 'Bryan C. Russell', 'Matthew Fisher', 'Mathieu Aubry', 'Vladimir G. Kim'] | 2019-08-13 | learning-elementary-structures-for-3d-shape-1 | http://papers.nips.cc/paper/8962-learning-elementary-structures-for-3d-shape-generation-and-matching | http://papers.nips.cc/paper/8962-learning-elementary-structures-for-3d-shape-generation-and-matching.pdf | neurips-2019-12 | ['3d-dense-shape-correspondence', '3d-shape-generation'] | ['computer-vision', 'computer-vision'] | [ 1.65672049e-01 5.46813607e-01 1.04779296e-01 -4.13004756e-01
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a5a8d6bc-4bff-476a-8985-f6f533b71d25 | ambiguity-resistant-semi-supervised-learning | 2303.14960 | null | https://arxiv.org/abs/2303.14960v1 | https://arxiv.org/pdf/2303.14960v1.pdf | Ambiguity-Resistant Semi-Supervised Learning for Dense Object Detection | With basic Semi-Supervised Object Detection (SSOD) techniques, one-stage detectors generally obtain limited promotions compared with two-stage clusters. We experimentally find that the root lies in two kinds of ambiguities: (1) Selection ambiguity that selected pseudo labels are less accurate, since classification scores cannot properly represent the localization quality. (2) Assignment ambiguity that samples are matched with improper labels in pseudo-label assignment, as the strategy is misguided by missed objects and inaccurate pseudo boxes. To tackle these problems, we propose a Ambiguity-Resistant Semi-supervised Learning (ARSL) for one-stage detectors. Specifically, to alleviate the selection ambiguity, Joint-Confidence Estimation (JCE) is proposed to jointly quantifies the classification and localization quality of pseudo labels. As for the assignment ambiguity, Task-Separation Assignment (TSA) is introduced to assign labels based on pixel-level predictions rather than unreliable pseudo boxes. It employs a "divide-and-conquer" strategy and separately exploits positives for the classification and localization task, which is more robust to the assignment ambiguity. Comprehensive experiments demonstrate that ARSL effectively mitigates the ambiguities and achieves state-of-the-art SSOD performance on MS COCO and PASCAL VOC. Codes can be found at https://github.com/PaddlePaddle/PaddleDetection. | ['Jingdong Wang', 'Errui Ding', 'Xiaomao Li', 'Junyu Han', 'Xiao Tan', 'Wei zhang', 'Xiangru Lin', 'Weiming Zhang', 'Chang Liu'] | 2023-03-27 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Liu_Ambiguity-Resistant_Semi-Supervised_Learning_for_Dense_Object_Detection_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Liu_Ambiguity-Resistant_Semi-Supervised_Learning_for_Dense_Object_Detection_CVPR_2023_paper.pdf | cvpr-2023-1 | ['dense-object-detection', 'semi-supervised-object-detection', 'pseudo-label'] | ['computer-vision', 'computer-vision', 'miscellaneous'] | [ 2.63934433e-01 -1.73949644e-01 -2.76223868e-01 -4.41982210e-01
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92908929-944b-4ffd-9818-21ed76cb3d27 | a-mask-based-adversarial-defense-scheme | 2204.11837 | null | https://arxiv.org/abs/2204.11837v1 | https://arxiv.org/pdf/2204.11837v1.pdf | A Mask-Based Adversarial Defense Scheme | Adversarial attacks hamper the functionality and accuracy of Deep Neural Networks (DNNs) by meddling with subtle perturbations to their inputs.In this work, we propose a new Mask-based Adversarial Defense scheme (MAD) for DNNs to mitigate the negative effect from adversarial attacks. To be precise, our method promotes the robustness of a DNN by randomly masking a portion of potential adversarial images, and as a result, the %classification result output of the DNN becomes more tolerant to minor input perturbations. Compared with existing adversarial defense techniques, our method does not need any additional denoising structure, nor any change to a DNN's design. We have tested this approach on a collection of DNN models for a variety of data sets, and the experimental results confirm that the proposed method can effectively improve the defense abilities of the DNNs against all of the tested adversarial attack methods. In certain scenarios, the DNN models trained with MAD have improved classification accuracy by as much as 20% to 90% compared to the original models that are given adversarial inputs. | ['Liangda Fang', 'Fangzhen Zhao', 'Chenyi Zhang', 'Weizhen Xu'] | 2022-04-21 | null | null | null | null | ['adversarial-defense'] | ['adversarial'] | [ 2.49660343e-01 1.22683026e-01 5.07921934e-01 -2.91559905e-01
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f5b6ae77-3a55-4752-bae6-1ddaeb1aa184 | correlated-quantization-for-distributed-mean | 2203.04925 | null | https://arxiv.org/abs/2203.04925v2 | https://arxiv.org/pdf/2203.04925v2.pdf | Correlated quantization for distributed mean estimation and optimization | We study the problem of distributed mean estimation and optimization under communication constraints. We propose a correlated quantization protocol whose leading term in the error guarantee depends on the mean deviation of data points rather than only their absolute range. The design doesn't need any prior knowledge on the concentration property of the dataset, which is required to get such dependence in previous works. We show that applying the proposed protocol as sub-routine in distributed optimization algorithms leads to better convergence rates. We also prove the optimality of our protocol under mild assumptions. Experimental results show that our proposed algorithm outperforms existing mean estimation protocols on a diverse set of tasks. | ['Felix Yu', 'Jae Hun Ro', 'Ziteng Sun', 'Ananda Theertha Suresh'] | 2022-03-09 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [ 1.63417414e-01 -1.21081188e-01 -1.60263464e-01 -5.29302657e-01
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b12272cb-b53e-4ec5-94ad-c6cab491e124 | qurantree-jl-a-julia-package-for-quranic | null | null | https://aclanthology.org/2021.wanlp-1.22 | https://aclanthology.org/2021.wanlp-1.22.pdf | QuranTree.jl: A Julia Package for Quranic Arabic Corpus | QuranTree.jl is an open-source package for working with the Quranic Arabic Corpus (Dukes and Habash, 2010). It aims to provide Julia APIs as an alternative to the Java APIs of JQuranTree. QuranTree.jl currently offers functionalities for intuitive indexing of chapters, verses, words and parts of words of the Qur’an; for creating custom transliteration; for character dediacritization and normalization; and, for handling the morphological features. Lastly, it can work well with Julia’s TextAnalysis.jl and Python’s CAMeL Tools. | ['Al-Ahmadgaid Asaad'] | null | null | null | null | eacl-wanlp-2021-4 | ['transliteration'] | ['natural-language-processing'] | [-6.15601897e-01 -3.15928131e-01 1.44257382e-01 6.92553632e-03
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e39e97ba-b134-452f-ad26-6b75e4a0caf0 | gaia-search-hugging-face-and-pyserini | 2306.01481 | null | https://arxiv.org/abs/2306.01481v1 | https://arxiv.org/pdf/2306.01481v1.pdf | GAIA Search: Hugging Face and Pyserini Interoperability for NLP Training Data Exploration | Noticing the urgent need to provide tools for fast and user-friendly qualitative analysis of large-scale textual corpora of the modern NLP, we propose to turn to the mature and well-tested methods from the domain of Information Retrieval (IR) - a research field with a long history of tackling TB-scale document collections. We discuss how Pyserini - a widely used toolkit for reproducible IR research can be integrated with the Hugging Face ecosystem of open-source AI libraries and artifacts. We leverage the existing functionalities of both platforms while proposing novel features further facilitating their integration. Our goal is to give NLP researchers tools that will allow them to develop retrieval-based instrumentation for their data analytics needs with ease and agility. We include a Jupyter Notebook-based walk through the core interoperability features, available on GitHub at https://github.com/huggingface/gaia. We then demonstrate how the ideas we present can be operationalized to create a powerful tool for qualitative data analysis in NLP. We present GAIA Search - a search engine built following previously laid out principles, giving access to four popular large-scale text collections. GAIA serves a dual purpose of illustrating the potential of methodologies we discuss but also as a standalone qualitative analysis tool that can be leveraged by NLP researchers aiming to understand datasets prior to using them in training. GAIA is hosted live on Hugging Face Spaces - https://huggingface.co/spaces/spacerini/gaia. | ['Jimmy Lin', 'Martin Potthast', 'Stella Biderman', 'Hailey Schoelkopf', 'Xinyu Zhang', 'Akintunde Oladipo', 'Christopher Akiki', 'Odunayo Ogundepo', 'Aleksandra Piktus'] | 2023-06-02 | null | null | null | null | ['information-retrieval'] | ['natural-language-processing'] | [-3.18106800e-01 2.97641810e-02 -3.89286309e-01 -2.76549011e-01
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7bcb3c55-846d-4c19-8c6b-849f9777ce48 | self-supervision-by-prediction-for-object | 2103.05669 | null | https://arxiv.org/abs/2103.05669v1 | https://arxiv.org/pdf/2103.05669v1.pdf | Self-Supervision by Prediction for Object Discovery in Videos | Despite their irresistible success, deep learning algorithms still heavily rely on annotated data. On the other hand, unsupervised settings pose many challenges, especially about determining the right inductive bias in diverse scenarios. One scalable solution is to make the model generate the supervision for itself by leveraging some part of the input data, which is known as self-supervised learning. In this paper, we use the prediction task as self-supervision and build a novel object-centric model for image sequence representation. In addition to disentangling the notion of objects and the motion dynamics, our compositional structure explicitly handles occlusion and inpaints inferred objects and background for the composition of the predicted frame. With the aid of auxiliary loss functions that promote spatially and temporally consistent object representations, our self-supervised framework can be trained without the help of any manual annotation or pretrained network. Initial experiments confirm that the proposed pipeline is a promising step towards object-centric video prediction. | ['Pascal Frossard', 'Beril Besbinar'] | 2021-03-09 | null | null | null | null | ['object-discovery-in-videos'] | ['computer-vision'] | [ 2.84378171e-01 2.71956533e-01 -4.22878802e-01 -4.89552468e-01
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73f27709-e374-4f50-a26e-2ada8e06ee8f | potential-of-deep-features-for-opinion | 1911.11903 | null | https://arxiv.org/abs/1911.11903v1 | https://arxiv.org/pdf/1911.11903v1.pdf | Potential of deep features for opinion-unaware, distortion-unaware, no-reference image quality assessment | Image Quality Assessment algorithms predict a quality score for a pristine or distorted input image, such that it correlates with human opinion. Traditional methods required a non-distorted "reference" version of the input image to compare with, in order to predict this score. However, recent "No-reference" methods circumvent this requirement by modelling the distribution of clean image features, thereby making them more suitable for practical use. However, majority of such methods either use hand-crafted features or require training on human opinion scores (supervised learning), which are difficult to obtain and standardise. We explore the possibility of using deep features instead, particularly, the encoded (bottleneck) feature maps of a Convolutional Autoencoder neural network architecture. Also, we do not train the network on subjective scores (unsupervised learning). The primary requirements for an IQA method are monotonic increase in predicted scores with increasing degree of input image distortion, and consistent ranking of images with the same distortion type and content, but different distortion levels. Quantitative experiments using the Pearson, Kendall and Spearman correlation scores on a diverse set of images show that our proposed method meets the above requirements better than the state-of-art method (which uses hand-crafted features) for three types of distortions: blurring, noise and compression artefacts. This demonstrates the potential for future research in this relatively unexplored sub-area within IQA. | ['Giuseppe Valenzise', 'Irene Cheng', 'Subhayan Mukherjee'] | 2019-11-27 | null | null | null | null | ['no-reference-image-quality-assessment'] | ['computer-vision'] | [ 3.22644979e-01 -3.04872781e-01 2.16323093e-01 -7.18073189e-01
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339cda65-0944-4170-9430-014233cb1ce8 | assessing-the-applicability-of-authorship | 1906.10551 | null | https://arxiv.org/abs/1906.10551v1 | https://arxiv.org/pdf/1906.10551v1.pdf | Assessing the Applicability of Authorship Verification Methods | Authorship verification (AV) is a research subject in the field of digital text forensics that concerns itself with the question, whether two documents have been written by the same person. During the past two decades, an increasing number of proposed AV approaches can be observed. However, a closer look at the respective studies reveals that the underlying characteristics of these methods are rarely addressed, which raises doubts regarding their applicability in real forensic settings. The objective of this paper is to fill this gap by proposing clear criteria and properties that aim to improve the characterization of existing and future AV approaches. Based on these properties, we conduct three experiments using 12 existing AV approaches, including the current state of the art. The examined methods were trained, optimized and evaluated on three self-compiled corpora, where each corpus focuses on a different aspect of applicability. Our results indicate that part of the methods are able to cope with very challenging verification cases such as 250 characters long informal chat conversations (72.7% accuracy) or cases in which two scientific documents were written at different times with an average difference of 15.6 years (> 75% accuracy). However, we also identified that all involved methods are prone to cross-topic verification cases. | ['Lukas Graner', 'Christian Winter', 'Oren Halvani'] | 2019-06-24 | null | null | null | null | ['authorship-verification'] | ['natural-language-processing'] | [ 2.71404833e-01 1.25021011e-01 8.85441378e-02 -1.65499244e-02
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4922c59d-1674-4aa0-ad2e-b52c5064138d | predicting-customers-gender-and-age-depending | 1903.06756 | null | http://arxiv.org/abs/1903.06756v1 | http://arxiv.org/pdf/1903.06756v1.pdf | Predicting customer's gender and age depending on mobile phone data | In the age of data driven solution, the customer demographic attributes, such
as gender and age, play a core role that may enable companies to enhance the
offers of their services and target the right customer in the right time and
place. In the marketing campaign, the companies want to target the real user of
the GSM (global system for mobile communications), not the line owner. Where
sometimes they may not be the same. This work proposes a method that predicts
users' gender and age based on their behavior, services and contract
information. We used call detail records (CDRs), customer relationship
management (CRM) and billing information as a data source to analyze telecom
customer behavior, and applied different types of machine learning algorithms
to provide marketing campaigns with more accurate information about customer
demographic attributes. This model is built using reliable data set of 18,000
users provided by SyriaTel Telecom Company, for training and testing. The model
applied by using big data technology and achieved 85.6% accuracy in terms of
user gender prediction and 65.5% of user age prediction. The main contribution
of this work is the improvement in the accuracy in terms of user gender
prediction and user age prediction based on mobile phone data and end-to-end
solution that approaches customer data from multiple aspects in the telecom
domain. | ['Kadan Aljoumaa', 'Assef Jafar', 'Ibrahim Mousa AlZuabi'] | 2019-02-20 | null | null | null | null | ['gender-prediction'] | ['computer-vision'] | [-3.70113343e-01 2.51874179e-01 -1.97101906e-01 -9.11889195e-01
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3fcc25bb-2b43-4c45-bdcd-2dae29ef3525 | pcrlv2-a-unified-visual-information | 2301.00772 | null | https://arxiv.org/abs/2301.00772v1 | https://arxiv.org/pdf/2301.00772v1.pdf | PCRLv2: A Unified Visual Information Preservation Framework for Self-supervised Pre-training in Medical Image Analysis | Recent advances in self-supervised learning (SSL) in computer vision are primarily comparative, whose goal is to preserve invariant and discriminative semantics in latent representations by comparing siamese image views. However, the preserved high-level semantics do not contain enough local information, which is vital in medical image analysis (e.g., image-based diagnosis and tumor segmentation). To mitigate the locality problem of comparative SSL, we propose to incorporate the task of pixel restoration for explicitly encoding more pixel-level information into high-level semantics. We also address the preservation of scale information, a powerful tool in aiding image understanding but has not drawn much attention in SSL. The resulting framework can be formulated as a multi-task optimization problem on the feature pyramid. Specifically, we conduct multi-scale pixel restoration and siamese feature comparison in the pyramid. In addition, we propose non-skip U-Net to build the feature pyramid and develop sub-crop to replace multi-crop in 3D medical imaging. The proposed unified SSL framework (PCRLv2) surpasses its self-supervised counterparts on various tasks, including brain tumor segmentation (BraTS 2018), chest pathology identification (ChestX-ray, CheXpert), pulmonary nodule detection (LUNA), and abdominal organ segmentation (LiTS), sometimes outperforming them by large margins with limited annotations. | ['Yizhou Yu', 'Sibei Yang', 'Chaoqi Chen', 'Chixiang Lu', 'Hong-Yu Zhou'] | 2023-01-02 | null | null | null | null | ['tumor-segmentation', 'brain-tumor-segmentation'] | ['computer-vision', 'medical'] | [ 5.06194413e-01 6.50353655e-02 -5.20679355e-01 -2.62748122e-01
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abad8cbd-0da8-4bb4-beec-ff2b008e3fd8 | a-dataset-and-benchmark-for-automatically | 2206.05442 | null | https://arxiv.org/abs/2206.05442v7 | https://arxiv.org/pdf/2206.05442v7.pdf | From Human Days to Machine Seconds: Automatically Answering and Generating Machine Learning Final Exams | A final exam in machine learning at a top institution such as MIT, Harvard, or Cornell typically takes faculty days to write, and students hours to solve. We demonstrate that large language models pass machine learning finals at a human level, on finals available online after the models were trained, and automatically generate new human-quality final exam questions in seconds. Previous work has developed program synthesis and few-shot learning methods to solve university-level problem set questions in mathematics and STEM courses. In this work, we develop and compare methods that solve final exams, which differ from problem sets in several ways: the questions are longer, have multiple parts, are more complicated, and span a broader set of topics. We curate a dataset and benchmark of questions from machine learning final exams available online and code for answering these questions and generating new questions. We show how to generate new questions from other questions and course notes. For reproducibility and future research on this final exam benchmark, we use automatic checkers for multiple-choice, numeric, and questions with expression answers. We perform ablation studies comparing zero-shot learning with few-shot learning and chain-of-thought prompting using GPT-3, OPT, Codex, and ChatGPT across machine learning topics and find that few-shot learning methods perform best. We highlight the transformative potential of language models to streamline the writing and solution of large-scale assessments, significantly reducing the workload from human days to mere machine seconds. Our results suggest that rather than banning large language models such as ChatGPT in class, instructors should teach students to harness them by asking students meta-questions about correctness, completeness, and originality of the responses generated, encouraging critical thinking in academic studies. | ['Madeleine Udell', 'Sage Simhon', 'Gregory Hunter', 'Saisamrit Surbehera', 'Keith Tyser', 'Reece Shuttleworth', 'Sarah J. Zhang', 'Iddo Drori', 'Pedro Lantigua', 'Zad Chin', 'Darnell Granberry', 'Sathwik Karnik', 'Leonard Tang', 'Yann Hicke', 'Derek Austin', 'Sarah Zhang'] | 2022-06-11 | null | null | null | null | ['program-synthesis'] | ['computer-code'] | [ 1.32623553e-01 2.05226481e-01 6.90133944e-02 -1.72555357e-01
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1b6bc44e-612e-4183-aae4-29e8c4d77376 | scientific-table-search-using-keyword-queries | 1707.03423 | null | http://arxiv.org/abs/1707.03423v1 | http://arxiv.org/pdf/1707.03423v1.pdf | Scientific Table Search Using Keyword Queries | Tables are common and important in scientific documents, yet most text-based
document search systems do not capture structures and semantics specific to
tables. How to bridge different types of mismatch between keywords queries and
scientific tables and what influences ranking quality needs to be carefully
investigated. This paper considers the structure of tables and gives different
emphasis to table components. On the query side, thanks to external knowledge
such as knowledge bases and ontologies, key concepts are extracted and used to
build structured queries, and target quantity types are identified and used to
expand original queries. A probabilistic framework is proposed to incorporate
structural and semantic information from both query and table sides. We also
construct and release TableArXiv, a high quality dataset with 105 queries and
corresponding relevance judgements for scientific table search. Experiments
demonstrate significantly higher accuracy overall and at the top of the
rankings than several baseline methods. | ['Callan Jamie', 'Gao Kyle Yingkai'] | 2017-07-11 | null | null | null | null | ['table-search'] | ['natural-language-processing'] | [-1.62207276e-01 4.19623181e-02 -5.92179775e-01 -2.82328427e-01
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1476f97f-9458-4e67-b126-63a56c65a68e | multi-objective-population-based-training | 2306.01436 | null | https://arxiv.org/abs/2306.01436v1 | https://arxiv.org/pdf/2306.01436v1.pdf | Multi-Objective Population Based Training | Population Based Training (PBT) is an efficient hyperparameter optimization algorithm. PBT is a single-objective algorithm, but many real-world hyperparameter optimization problems involve two or more conflicting objectives. In this work, we therefore introduce a multi-objective version of PBT, MO-PBT. Our experiments on diverse multi-objective hyperparameter optimization problems (Precision/Recall, Accuracy/Fairness, Accuracy/Adversarial Robustness) show that MO-PBT outperforms random search, single-objective PBT, and the state-of-the-art multi-objective hyperparameter optimization algorithm MO-ASHA. | ['Peter A. N. Bosman', 'Tanja Alderliesten', 'Alexander Chebykin', 'Arkadiy Dushatskiy'] | 2023-06-02 | null | null | null | null | ['adversarial-robustness', 'hyperparameter-optimization'] | ['adversarial', 'methodology'] | [-2.73143500e-01 -4.46139276e-01 -6.77574873e-01 1.09796889e-01
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82ea32fb-4b92-4c74-8003-ce2d3b4996d1 | avasag-a-german-sign-language-translation | null | null | https://aclanthology.org/2021.mtsummit-at4ssl.5 | https://aclanthology.org/2021.mtsummit-at4ssl.5.pdf | AVASAG: A German Sign Language Translation System for Public Services (short paper) | This paper presents an overview of AVASAG; an ongoing applied-research project developing a text-to-sign-language translation system for public services. We describe the scientific innovation points (geometry-based SL-description, 3D animation and video corpus, simplified annotation scheme, motion capture strategy) and the overall translation pipeline. | ['Alexander Stricker', 'Dieter Wallach', 'Martin Misiak', 'Yvonne Kossel', 'Marcel Hauck', 'Yasser Hamidullah', 'Patrick Gebhard', 'Arnulph Fuhrmann', 'Christian Dold', 'Stephan Busemann', 'Elisabeth André', 'Sonja Wecker', 'Kristoffer Waldow', 'Amelie Unger', 'Corinna Jäger', 'Cristina España-Bonet', 'Lucas Bernhard', 'Judith Bauerdiek', 'Fabrizio Nunnari'] | null | null | null | null | mtsummit-2021-8 | ['sign-language-translation'] | ['computer-vision'] | [ 3.27894509e-01 -1.06336646e-01 -5.81077337e-01 -2.51522005e-01
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2dcb3689-1860-4389-be0c-168a243a85b4 | llm-pruner-on-the-structural-pruning-of-large | 2305.11627 | null | https://arxiv.org/abs/2305.11627v2 | https://arxiv.org/pdf/2305.11627v2.pdf | LLM-Pruner: On the Structural Pruning of Large Language Models | Large language models (LLMs) have shown remarkable capabilities in language understanding and generation. However, such impressive capability typically comes with a substantial model size, which presents significant challenges in both the deployment, inference, and training stages. With LLM being a general-purpose task solver, we explore its compression in a task-agnostic manner, which aims to preserve the multi-task solving and language generation ability of the original LLM. One challenge to achieving this is the enormous size of the training corpus of LLM, which makes both data transfer and model post-training over-burdensome. Thus, we tackle the compression of LLMs within the bound of two constraints: being task-agnostic and minimizing the reliance on the original training dataset. Our method, named LLM-Pruner, adopts structural pruning that selectively removes non-critical coupled structures based on gradient information, maximally preserving the majority of the LLM's functionality. To this end, the performance of pruned models can be efficiently recovered through tuning techniques, LoRA, in merely 3 hours, requiring only 50K data. We validate the LLM-Pruner on three LLMs, including LLaMA, Vicuna, and ChatGLM, and demonstrate that the compressed models still exhibit satisfactory capabilities in zero-shot classification and generation. The code is available at: https://github.com/horseee/LLM-Pruner | ['Xinchao Wang', 'Gongfan Fang', 'Xinyin Ma'] | 2023-05-19 | null | null | null | null | ['model-compression'] | ['methodology'] | [ 2.72456348e-01 2.42638469e-01 -2.25552827e-01 -1.56806976e-01
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079876be-2d63-4e35-934a-8a5d9a4ae90c | a-robust-classifier-under-missing-not-at | 2305.15641 | null | https://arxiv.org/abs/2305.15641v1 | https://arxiv.org/pdf/2305.15641v1.pdf | A Robust Classifier Under Missing-Not-At-Random Sample Selection Bias | The shift between the training and testing distributions is commonly due to sample selection bias, a type of bias caused by non-random sampling of examples to be included in the training set. Although there are many approaches proposed to learn a classifier under sample selection bias, few address the case where a subset of labels in the training set are missing-not-at-random (MNAR) as a result of the selection process. In statistics, Greene's method formulates this type of sample selection with logistic regression as the prediction model. However, we find that simply integrating this method into a robust classification framework is not effective for this bias setting. In this paper, we propose BiasCorr, an algorithm that improves on Greene's method by modifying the original training set in order for a classifier to learn under MNAR sample selection bias. We provide theoretical guarantee for the improvement of BiasCorr over Greene's method by analyzing its bias. Experimental results on real-world datasets demonstrate that BiasCorr produces robust classifiers and can be extended to outperform state-of-the-art classifiers that have been proposed to train under sample selection bias. | ['Xintao Wu', 'Wei Du', 'Wen Huang', 'Huy Mai'] | 2023-05-25 | null | null | null | null | ['selection-bias'] | ['natural-language-processing'] | [ 4.39537853e-01 -2.52740481e-03 -8.65314901e-01 -1.01668608e+00
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a0d5d65e-ba01-4e29-bc9d-25e013bd55fc | personalized-prediction-of-recurrent-stress | 2307.03337 | null | https://arxiv.org/abs/2307.03337v1 | https://arxiv.org/pdf/2307.03337v1.pdf | Personalized Prediction of Recurrent Stress Events Using Self-Supervised Learning on Multimodal Time-Series Data | Chronic stress can significantly affect physical and mental health. The advent of wearable technology allows for the tracking of physiological signals, potentially leading to innovative stress prediction and intervention methods. However, challenges such as label scarcity and data heterogeneity render stress prediction difficult in practice. To counter these issues, we have developed a multimodal personalized stress prediction system using wearable biosignal data. We employ self-supervised learning (SSL) to pre-train the models on each subject's data, allowing the models to learn the baseline dynamics of the participant's biosignals prior to fine-tuning the stress prediction task. We test our model on the Wearable Stress and Affect Detection (WESAD) dataset, demonstrating that our SSL models outperform non-SSL models while utilizing less than 5% of the annotations. These results suggest that our approach can personalize stress prediction to each user with minimal annotations. This paradigm has the potential to enable personalized prediction of a variety of recurring health events using complex multimodal data streams. | ['Peter Washington', 'Tanvir Islam'] | 2023-07-07 | null | null | null | null | ['self-supervised-learning'] | ['computer-vision'] | [ 5.54599226e-01 1.65123388e-01 -3.15307021e-01 -8.51638973e-01
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ae6e41b2-c0ba-43c3-8282-04b5d1c087fd | a-neuromorphic-vision-based-measurement-for | 2206.11541 | null | https://arxiv.org/abs/2206.11541v2 | https://arxiv.org/pdf/2206.11541v2.pdf | A Neuromorphic Vision-Based Measurement for Robust Relative Localization in Future Space Exploration Missions | Space exploration has witnessed revolutionary changes upon landing of the Perseverance Rover on the Martian surface and demonstrating the first flight beyond Earth by the Mars helicopter, Ingenuity. During their mission on Mars, Perseverance Rover and Ingenuity collaboratively explore the Martian surface, where Ingenuity scouts terrain information for rover's safe traversability. Hence, determining the relative poses between both the platforms is of paramount importance for the success of this mission. Driven by this necessity, this work proposes a robust relative localization system based on a fusion of neuromorphic vision-based measurements (NVBMs) and inertial measurements. The emergence of neuromorphic vision triggered a paradigm shift in the computer vision community, due to its unique working principle delineated with asynchronous events triggered by variations of light intensities occurring in the scene. This implies that observations cannot be acquired in static scenes due to illumination invariance. To circumvent this limitation, high frequency active landmarks are inserted in the scene to guarantee consistent event firing. These landmarks are adopted as salient features to facilitate relative localization. A novel event-based landmark identification algorithm using Gaussian Mixture Models (GMM) is developed for matching the landmarks correspondences formulating our NVBMs. The NVBMs are fused with inertial measurements in proposed state estimators, landmark tracking Kalman filter (LTKF) and translation decoupled Kalman filter (TDKF) for landmark tracking and relative localization, respectively. The proposed system was tested in a variety of experiments and has outperformed state-of-the-art approaches in accuracy and range. | ['Lakmal Seneviratne', 'Yahya Zweiri', 'Rana Azzam', 'Abdulla Ayyad', 'Mohammed Wahbah', 'Muhammed Humais', 'Mohammed Chehadah', 'Mohammed Salah'] | 2022-06-23 | null | null | null | null | ['landmark-tracking'] | ['computer-vision'] | [ 6.14427850e-02 -5.27331054e-01 5.93152046e-02 1.24407122e-02
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f6ea4127-806e-4f3a-bd2d-d1eaa9273872 | conditioning-diffusion-models-via-attributes | 2306.00914 | null | https://arxiv.org/abs/2306.00914v1 | https://arxiv.org/pdf/2306.00914v1.pdf | Conditioning Diffusion Models via Attributes and Semantic Masks for Face Generation | Deep generative models have shown impressive results in generating realistic images of faces. GANs managed to generate high-quality, high-fidelity images when conditioned on semantic masks, but they still lack the ability to diversify their output. Diffusion models partially solve this problem and are able to generate diverse samples given the same condition. In this paper, we propose a multi-conditioning approach for diffusion models via cross-attention exploiting both attributes and semantic masks to generate high-quality and controllable face images. We also studied the impact of applying perceptual-focused loss weighting into the latent space instead of the pixel space. Our method extends the previous approaches by introducing conditioning on more than one set of features, guaranteeing a more fine-grained control over the generated face images. We evaluate our approach on the CelebA-HQ dataset, and we show that it can generate realistic and diverse samples while allowing for fine-grained control over multiple attributes and semantic regions. Additionally, we perform an ablation study to evaluate the impact of different conditioning strategies on the quality and diversity of the generated images. | ['Giuseppe Lisanti', 'Nico Giambi'] | 2023-06-01 | null | null | null | null | ['face-generation'] | ['computer-vision'] | [ 2.75863647e-01 1.68686450e-01 3.23506355e-01 -3.67665976e-01
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eaee080b-7589-4bf0-a86d-ab1b60312784 | page-a-position-aware-graph-based-model-for | 2303.01795 | null | https://arxiv.org/abs/2303.01795v1 | https://arxiv.org/pdf/2303.01795v1.pdf | PAGE: A Position-Aware Graph-Based Model for Emotion Cause Entailment in Conversation | Conversational Causal Emotion Entailment (C2E2) is a task that aims at recognizing the causes corresponding to a target emotion in a conversation. The order of utterances in the conversation affects the causal inference. However, most current position encoding strategies ignore the order relation among utterances and speakers. To address the issue, we devise a novel position-aware graph to encode the entire conversation, fully modeling causal relations among utterances. The comprehensive experiments show that our method consistently achieves state-of-the-art performance on two challenging test sets, proving the effectiveness of our model. Our source code is available on Github: https://github.com/XiaojieGu/PAGE. | ['Shangxin Li', 'Lin Sun', 'Renze Lou', 'Xiaojie Gu'] | 2023-03-03 | null | null | null | null | ['causal-emotion-entailment'] | ['natural-language-processing'] | [ 9.97836739e-02 5.03446996e-01 -4.48543131e-01 -6.83009267e-01
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b2a13487-bf7d-4b15-b71a-e22aedb7036e | measuring-human-perception-to-improve-open | 2209.03519 | null | https://arxiv.org/abs/2209.03519v4 | https://arxiv.org/pdf/2209.03519v4.pdf | Measuring Human Perception to Improve Open Set Recognition | The human ability to recognize when an object belongs or does not belong to a particular vision task outperforms all open set recognition algorithms. Human perception as measured by the methods and procedures of visual psychophysics from psychology provides an additional data stream for algorithms that need to manage novelty. For instance, measured reaction time from human subjects can offer insight as to whether a class sample is prone to be confused with a different class -- known or novel. In this work, we designed and performed a large-scale behavioral experiment that collected over 200,000 human reaction time measurements associated with object recognition. The data collected indicated reaction time varies meaningfully across objects at the sample-level. We therefore designed a new psychophysical loss function that enforces consistency with human behavior in deep networks which exhibit variable reaction time for different images. As in biological vision, this approach allows us to achieve good open set recognition performance in regimes with limited labeled training data. Through experiments using data from ImageNet, significant improvement is observed when training Multi-Scale DenseNets with this new formulation: it significantly improved top-1 validation accuracy by 6.02%, top-1 test accuracy on known samples by 9.81%, and top-1 test accuracy on unknown samples by 33.18%. We compared our method to 10 open set recognition methods from the literature, which were all outperformed on multiple metrics. | ['Justin Dulay', 'Walter Scheirer', 'Derek Prijatelj', 'Jin Huang'] | 2022-09-08 | null | null | null | null | ['open-set-learning'] | ['miscellaneous'] | [ 2.93752939e-01 -1.65597931e-01 2.21048534e-01 -4.62415278e-01
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63eedcd0-8c5b-4a23-b166-70bb41bfeb16 | on-aligning-openie-extractions-with-knowledge | null | null | https://aclanthology.org/2020.eval4nlp-1.14 | https://aclanthology.org/2020.eval4nlp-1.14.pdf | On Aligning OpenIE Extractions with Knowledge Bases: A Case Study | Open information extraction (OIE) is the task of extracting relations and their corresponding arguments from a natural language text in un- supervised manner. Outputs of such systems are used for downstream tasks such as ques- tion answering and automatic knowledge base (KB) construction. Many of these downstream tasks rely on aligning OIE triples with refer- ence KBs. Such alignments are usually eval- uated w.r.t. a specific downstream task and, to date, no direct manual evaluation of such alignments has been performed. In this paper, we directly evaluate how OIE triples from the OPIEC corpus are related to the DBpedia KB w.r.t. information content. First, we investigate OPIEC triples and DBpedia facts having the same arguments by comparing the information on the OIE surface relation with the KB rela- tion. Second, we evaluate the expressibility of general OPIEC triples in DBpedia. We in- vestigate whether—and, if so, how—a given OIE triple can be mapped to a single KB fact. We found that such mappings are not always possible because the information in the OIE triples tends to be more specific. Our evalua- tion suggests, however, that significant part of OIE triples can be expressed by means of KB formulas instead of individual facts. | ['Christian Meilicke', 'Sven Hertling', 'Bhushan Kotnis', 'Rainer Gemulla', 'Kiril Gashteovski'] | null | null | null | null | emnlp-eval4nlp-2020-11 | ['open-information-extraction'] | ['natural-language-processing'] | [-1.83885708e-01 8.92357528e-01 -2.61726975e-01 -3.64572942e-01
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2b5d3182-7791-4dda-94d7-97161d9122ee | generative-adversarial-networks-based-on-1 | null | null | https://doi.org/10.3390/rs14143426 | https://www.mdpi.com/2072-4292/14/14/3426/pdf?version=1658469150 | Generative Adversarial Networks Based on Transformer Encoder and Convolution Block for Hyperspectral Image Classification | Nowadays, HSI classification can reach a high classification accuracy when given sufficient labeled samples as training set. However, the performances of existing methods decrease sharply when trained on few labeled samples. Existing methods in few-shot problems usually require another dataset in order to improve the classification accuracy. However, the cross-domain problem exists in these methods because of the significant spectral shift between target domain and source domain. Considering above issues, we propose a new method without requiring external dataset through combining a Generative Adversarial Network, Transformer Encoder and convolution block in a unified framework. The proposed method has both a global receptive field provided by Transformer Encoder and a local receptive field provided by convolution block. Experiments conducted on Indian Pines, PaviaU and KSC datasets demonstrate that our method exceeds the results of existing deep learning methods for hyperspectral image classification in the few-shot learning problem. | ['Licheng Jiao', 'Zheng Chen', 'Zhu Xiao', 'Jiawei Lu', 'Jing Bai'] | 2022-07-16 | null | null | null | remote-sensing-2022-7 | ['classification-of-hyperspectral-images', 'few-shot-image-classification'] | ['computer-vision', 'computer-vision'] | [ 5.27720392e-01 -5.19452751e-01 8.82039890e-02 -3.02595347e-01
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550d58f7-882d-4fde-b55a-104c40ec2913 | rethinking-the-compositionality-of-point | 2209.10318 | null | https://arxiv.org/abs/2209.10318v1 | https://arxiv.org/pdf/2209.10318v1.pdf | Rethinking the compositionality of point clouds through regularization in the hyperbolic space | Point clouds of 3D objects exhibit an inherent compositional nature where simple parts can be assembled into progressively more complex shapes to form whole objects. Explicitly capturing such part-whole hierarchy is a long-sought objective in order to build effective models, but its tree-like nature has made the task elusive. In this paper, we propose to embed the features of a point cloud classifier into the hyperbolic space and explicitly regularize the space to account for the part-whole hierarchy. The hyperbolic space is the only space that can successfully embed the tree-like nature of the hierarchy. This leads to substantial improvements in the performance of state-of-art supervised models for point cloud classification. | ['Enrico Magli', 'Diego Valsesia', 'Antonio Montanaro'] | 2022-09-21 | null | null | null | null | ['3d-point-cloud-classification', 'point-cloud-classification'] | ['computer-vision', 'computer-vision'] | [-1.56628609e-01 3.73858511e-02 -8.65556523e-02 -4.28633749e-01
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42df28dd-1696-47fd-995d-85d6af910572 | tackling-data-heterogeneity-in-federated | 2212.02758 | null | https://arxiv.org/abs/2212.02758v1 | https://arxiv.org/pdf/2212.02758v1.pdf | Tackling Data Heterogeneity in Federated Learning with Class Prototypes | Data heterogeneity across clients in federated learning (FL) settings is a widely acknowledged challenge. In response, personalized federated learning (PFL) emerged as a framework to curate local models for clients' tasks. In PFL, a common strategy is to develop local and global models jointly - the global model (for generalization) informs the local models, and the local models (for personalization) are aggregated to update the global model. A key observation is that if we can improve the generalization ability of local models, then we can improve the generalization of global models, which in turn builds better personalized models. In this work, we consider class imbalance, an overlooked type of data heterogeneity, in the classification setting. We propose FedNH, a novel method that improves the local models' performance for both personalization and generalization by combining the uniformity and semantics of class prototypes. FedNH initially distributes class prototypes uniformly in the latent space and smoothly infuses the class semantics into class prototypes. We show that imposing uniformity helps to combat prototype collapse while infusing class semantics improves local models. Extensive experiments were conducted on popular classification datasets under the cross-device setting. Our results demonstrate the effectiveness and stability of our method over recent works. | ['ran Xu', 'Lichao Sun', 'Shelby Heinecke', 'Junnan Li', 'Zeyuan Chen', 'Yutong Dai'] | 2022-12-06 | null | null | null | null | ['personalized-federated-learning'] | ['methodology'] | [-2.66958684e-01 -1.53621361e-01 -6.66811764e-01 -6.83818758e-01
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fe24bd25-9cc6-4ad0-bfa3-ac67a1a78f1d | distinct-label-representations-for-few-shot | null | null | https://aclanthology.org/2021.acl-short.105 | https://aclanthology.org/2021.acl-short.105.pdf | Distinct Label Representations for Few-Shot Text Classification | Few-shot text classification aims to classify inputs whose label has only a few examples. Previous studies overlooked the semantic relevance between label representations. Therefore, they are easily confused by labels that are relevant. To address this problem, we propose a method that generates distinct label representations that embed information specific to each label. Our method is applicable to conventional few-shot classification models. Experimental results show that our method significantly improved the performance of few-shot text classification across models and datasets. | ['Yuki Arase', 'Tomoyuki Kajiwara', 'Junya Takayama', 'Sora Ohashi'] | 2021-08-01 | null | null | null | acl-2021-5 | ['few-shot-text-classification'] | ['natural-language-processing'] | [ 4.62091357e-01 -2.72573275e-03 -6.13875866e-01 -7.09066868e-01
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d7f6c923-fa0f-48ca-8899-d7e8fb9c246f | optimal-energy-rationing-for-prepaid | 2304.09251 | null | https://arxiv.org/abs/2304.09251v1 | https://arxiv.org/pdf/2304.09251v1.pdf | Optimal Energy Rationing for Prepaid Electricity Customers | For a large (and recently increasing) number of households, affordability is a major hurdle in accessing sufficient electricity and avoiding service disconnections. For such households, in-home energy rationing, i.e. the need to actively prioritize how to use a limited amount of electricity, is an everyday reality. In this paper, we consider a particularly vulnerable group of customers, namely prepaid electricity customers, who are required to pay for their electricity a-priori. With this group of customers in mind, we propose an optimization-based energy management framework to effectively use a limited budget and avoid the disruptions and fees associated with disconnections. The framework considers forecasts of future use and knowledge of appliance power ratings to help customers prioritize and limit use of low-priority loads, with the goal of extending access to their critical loads. Importantly, the proposed management system has minimal requirements in terms of in-home hardware and remote communication, lending itself well to adoption across different regions and utility programs. Our case study demonstrates that by considering both current and future electricity consumption and more effectively managing access to low-priority loads, the proposed framework increases the value provided to customers and avoids disconnections. | ['Line A. Roald', 'Maitreyee Marathe'] | 2023-04-18 | null | null | null | null | ['energy-management'] | ['time-series'] | [-7.75283799e-02 3.08546633e-01 -3.38464975e-01 -2.38314778e-01
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2364308e-8998-45f5-b499-0dc363b62816 | a-mention-ranking-model-for-abstract-anaphora | 1706.02256 | null | http://arxiv.org/abs/1706.02256v2 | http://arxiv.org/pdf/1706.02256v2.pdf | A Mention-Ranking Model for Abstract Anaphora Resolution | Resolving abstract anaphora is an important, but difficult task for text
understanding. Yet, with recent advances in representation learning this task
becomes a more tangible aim. A central property of abstract anaphora is that it
establishes a relation between the anaphor embedded in the anaphoric sentence
and its (typically non-nominal) antecedent. We propose a mention-ranking model
that learns how abstract anaphors relate to their antecedents with an
LSTM-Siamese Net. We overcome the lack of training data by generating
artificial anaphoric sentence--antecedent pairs. Our model outperforms
state-of-the-art results on shell noun resolution. We also report first
benchmark results on an abstract anaphora subset of the ARRAU corpus. This
corpus presents a greater challenge due to a mixture of nominal and pronominal
anaphors and a greater range of confounders. We found model variants that
outperform the baselines for nominal anaphors, without training on individual
anaphor data, but still lag behind for pronominal anaphors. Our model selects
syntactically plausible candidates and -- if disregarding syntax --
discriminates candidates using deeper features. | ['Ana Marasović', 'Juri Opitz', 'Anette Frank', 'Leo Born'] | 2017-06-07 | a-mention-ranking-model-for-abstract-anaphora-1 | https://aclanthology.org/D17-1021 | https://aclanthology.org/D17-1021.pdf | emnlp-2017-9 | ['abstract-anaphora-resolution'] | ['natural-language-processing'] | [ 1.21925250e-01 4.34803963e-01 -6.36471391e-01 -5.46329260e-01
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47a69feb-bcca-49e5-9f1e-283f0c2d856e | on-using-the-two-way-cluster-robust-standard | 2301.13775 | null | https://arxiv.org/abs/2301.13775v1 | https://arxiv.org/pdf/2301.13775v1.pdf | On Using The Two-Way Cluster-Robust Standard Errors | Thousands of papers have reported two-way cluster-robust (TWCR) standard errors. However, the recent econometrics literature points out the potential non-gaussianity of two-way cluster sample means, and thus invalidity of the inference based on the TWCR standard errors. Fortunately, simulation studies nonetheless show that the gaussianity is rather common than exceptional. This paper provides theoretical support for this encouraging observation. Specifically, we derive a novel central limit theorem for two-way clustered triangular arrays that justifies the use of the TWCR under very mild and interpretable conditions. We, therefore, hope that this paper will provide a theoretical justification for the legitimacy of most, if not all, of the thousands of those empirical papers that have used the TWCR standard errors. We provide a guide in practice as to when a researcher can employ the TWCR standard errors. | ['Yuya Sasaki', 'Harold D Chiang'] | 2023-01-31 | null | null | null | null | ['econometrics'] | ['miscellaneous'] | [-1.53778255e-01 -2.25352064e-01 -1.26722589e-01 -3.58435899e-01
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2558ceeb-9249-4d3c-86d9-cd2d50349ed5 | understanding-compositional-data-augmentation | 2305.13658 | null | https://arxiv.org/abs/2305.13658v1 | https://arxiv.org/pdf/2305.13658v1.pdf | Understanding compositional data augmentation in automatic morphological inflection | Data augmentation techniques are widely used in low-resource automatic morphological inflection to address the issue of data sparsity. However, the full implications of these techniques remain poorly understood. In this study, we aim to shed light on the theoretical aspects of the data augmentation strategy StemCorrupt, a method that generates synthetic examples by randomly substituting stem characters in existing gold standard training examples. Our analysis uncovers that StemCorrupt brings about fundamental changes in the underlying data distribution, revealing inherent compositional concatenative structure. To complement our theoretical analysis, we investigate the data-efficiency of StemCorrupt. Through evaluation across a diverse set of seven typologically distinct languages, we demonstrate that selecting a subset of datapoints with both high diversity and high predictive uncertainty significantly enhances the data-efficiency of StemCorrupt compared to competitive baselines. Furthermore, we explore the impact of typological features on the choice of augmentation strategy and find that languages incorporating non-concatenativity, such as morphonological alternations, derive less benefit from synthetic examples with high predictive uncertainty. We attribute this effect to phonotactic violations induced by StemCorrupt, emphasizing the need for further research to ensure optimal performance across the entire spectrum of natural language morphology. | ['Miikka Silfverberg', 'Farhan Samir'] | 2023-05-23 | null | null | null | null | ['morphological-inflection'] | ['natural-language-processing'] | [ 5.20484388e-01 1.30035818e-01 -3.97645772e-01 -1.94994614e-01
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340f428d-51ae-4a01-9e97-e703f5aac5e1 | enhanced-multi-channel-graph-convolutional | null | null | https://aclanthology.org/2022.acl-long.212 | https://aclanthology.org/2022.acl-long.212.pdf | Enhanced Multi-Channel Graph Convolutional Network for Aspect Sentiment Triplet Extraction | Aspect Sentiment Triplet Extraction (ASTE) is an emerging sentiment analysis task. Most of the existing studies focus on devising a new tagging scheme that enables the model to extract the sentiment triplets in an end-to-end fashion. However, these methods ignore the relations between words for ASTE task. In this paper, we propose an Enhanced Multi-Channel Graph Convolutional Network model (EMC-GCN) to fully utilize the relations between words. Specifically, we first define ten types of relations for ASTE task, and then adopt a biaffine attention module to embed these relations as an adjacent tensor between words in a sentence. After that, our EMC-GCN transforms the sentence into a multi-channel graph by treating words and the relation adjacent tensor as nodes and edges, respectively. Thus, relation-aware node representations can be learnt. Furthermore, we consider diverse linguistic features to enhance our EMC-GCN model. Finally, we design an effective refining strategy on EMC-GCN for word-pair representation refinement, which considers the implicit results of aspect and opinion extraction when determining whether word pairs match or not. Extensive experimental results on the benchmark datasets demonstrate that the effectiveness and robustness of our proposed model, which outperforms state-of-the-art methods significantly. | ['Xiaojie Wang', 'Ruifan Li', 'Fangxiang Feng', 'Zepeng Zhai', 'Hao Chen'] | null | null | null | null | acl-2022-5 | ['aspect-sentiment-triplet-extraction'] | ['natural-language-processing'] | [-4.94325720e-02 -4.24159989e-02 -9.25077498e-02 -5.80501437e-01
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ac444dc6-9f3c-4f40-a86d-31e6284c55a7 | uv-based-3d-hand-object-reconstruction-with | 2211.13429 | null | https://arxiv.org/abs/2211.13429v1 | https://arxiv.org/pdf/2211.13429v1.pdf | UV-Based 3D Hand-Object Reconstruction with Grasp Optimization | We propose a novel framework for 3D hand shape reconstruction and hand-object grasp optimization from a single RGB image. The representation of hand-object contact regions is critical for accurate reconstructions. Instead of approximating the contact regions with sparse points, as in previous works, we propose a dense representation in the form of a UV coordinate map. Furthermore, we introduce inference-time optimization to fine-tune the grasp and improve interactions between the hand and the object. Our pipeline increases hand shape reconstruction accuracy and produces a vibrant hand texture. Experiments on datasets such as Ho3D, FreiHAND, and DexYCB reveal that our proposed method outperforms the state-of-the-art. | ['Angela Yao', 'Ping Chen', 'You Xie', 'Linlin Yang', 'Ziwei Yu'] | 2022-11-24 | null | null | null | null | ['object-reconstruction'] | ['computer-vision'] | [-2.38071665e-01 -2.46652395e-01 5.40868640e-02 -8.85326639e-02
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112424ff-dffe-42ab-aa44-16a3b5e775ca | adults-as-augmentations-for-children-in | 2202.05187 | null | https://arxiv.org/abs/2202.05187v1 | https://arxiv.org/pdf/2202.05187v1.pdf | Adults as Augmentations for Children in Facial Emotion Recognition with Contrastive Learning | Emotion recognition in children can help the early identification of, and intervention on, psychological complications that arise in stressful situations such as cancer treatment. Though deep learning models are increasingly being adopted, data scarcity is often an issue in pediatric medicine, including for facial emotion recognition in children. In this paper, we study the application of data augmentation-based contrastive learning to overcome data scarcity in facial emotion recognition for children. We explore the idea of ignoring generational gaps, by adding abundantly available adult data to pediatric data, to learn better representations. We investigate different ways by which adult facial expression images can be used alongside those of children. In particular, we propose to explicitly incorporate within each mini-batch adult images as augmentations for children's. Out of $84$ combinations of learning approaches and training set sizes, we find that supervised contrastive learning with the proposed training scheme performs best, reaching a test accuracy that typically surpasses the one of the second-best approach by 2% to 3%. Our results indicate that adult data can be considered to be a meaningful augmentation of pediatric data for the recognition of emotional facial expression in children, and open up the possibility for other applications of contrastive learning to improve pediatric care by complementing data of children with that of adults. | ['Peter A. N. Bosman', 'Tanja Alderliesten', 'Andrea De Lorenzo', 'Marco Virgolin'] | 2022-02-10 | null | null | null | null | ['facial-emotion-recognition'] | ['computer-vision'] | [ 3.83990288e-01 4.31079954e-01 -2.76306838e-01 -8.58632326e-01
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94a7bd97-f0ad-48f2-b4db-4dfeffc52b3e | fastdvdnet-towards-real-time-video-denoising | 1907.01361 | null | https://arxiv.org/abs/1907.01361v2 | https://arxiv.org/pdf/1907.01361v2.pdf | FastDVDnet: Towards Real-Time Deep Video Denoising Without Flow Estimation | In this paper, we propose a state-of-the-art video denoising algorithm based on a convolutional neural network architecture. Until recently, video denoising with neural networks had been a largely under explored domain, and existing methods could not compete with the performance of the best patch-based methods. The approach we introduce in this paper, called FastDVDnet, shows similar or better performance than other state-of-the-art competitors with significantly lower computing times. In contrast to other existing neural network denoisers, our algorithm exhibits several desirable properties such as fast runtimes, and the ability to handle a wide range of noise levels with a single network model. The characteristics of its architecture make it possible to avoid using a costly motion compensation stage while achieving excellent performance. The combination between its denoising performance and lower computational load makes this algorithm attractive for practical denoising applications. We compare our method with different state-of-art algorithms, both visually and with respect to objective quality metrics. | ['Julie Delon', 'Thomas Veit', 'Matias Tassano'] | 2019-07-01 | fastdvdnet-towards-real-time-deep-video | http://openaccess.thecvf.com/content_CVPR_2020/html/Tassano_FastDVDnet_Towards_Real-Time_Deep_Video_Denoising_Without_Flow_Estimation_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Tassano_FastDVDnet_Towards_Real-Time_Deep_Video_Denoising_Without_Flow_Estimation_CVPR_2020_paper.pdf | cvpr-2020-6 | ['video-denoising'] | ['computer-vision'] | [ 1.08190961e-01 -4.74173933e-01 3.44119608e-01 -2.33128428e-01
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b753483f-4cfa-48d1-849e-26f04d79c914 | on-rank-energy-statistics-via-optimal | 2302.07964 | null | https://arxiv.org/abs/2302.07964v1 | https://arxiv.org/pdf/2302.07964v1.pdf | On Rank Energy Statistics via Optimal Transport: Continuity, Convergence, and Change Point Detection | This paper considers the use of recently proposed optimal transport-based multivariate test statistics, namely rank energy and its variant the soft rank energy derived from entropically regularized optimal transport, for the unsupervised nonparametric change point detection (CPD) problem. We show that the soft rank energy enjoys both fast rates of statistical convergence and robust continuity properties which lead to strong performance on real datasets. Our theoretical analyses remove the need for resampling and out-of-sample extensions previously required to obtain such rates. In contrast the rank energy suffers from the curse of dimensionality in statistical estimation and moreover can signal a change point from arbitrarily small perturbations, which leads to a high rate of false alarms in CPD. Additionally, under mild regularity conditions, we quantify the discrepancy between soft rank energy and rank energy in terms of the regularization parameter. Finally, we show our approach performs favorably in numerical experiments compared to several other optimal transport-based methods as well as maximum mean discrepancy. | ['Shuchin Aeron', 'James M. Murphy', 'Shoaib Bin Masud', 'Matthew Werenski'] | 2023-02-15 | null | null | null | null | ['change-point-detection'] | ['time-series'] | [ 4.25745174e-02 -1.77872837e-01 7.12718442e-02 -7.17768585e-03
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923d712b-ce0d-4efb-954f-6be75b5954fe | panoramic-annular-localizer-tackling-the | 1905.05425 | null | https://arxiv.org/abs/1905.05425v2 | https://arxiv.org/pdf/1905.05425v2.pdf | Panoramic Annular Localizer: Tackling the Variation Challenges of Outdoor Localization Using Panoramic Annular Images and Active Deep Descriptors | Visual localization is an attractive problem that estimates the camera localization from database images based on the query image. It is a crucial task for various applications, such as autonomous vehicles, assistive navigation and augmented reality. The challenging issues of the task lie in various appearance variations between query and database images, including illumination variations, dynamic object variations and viewpoint variations. In order to tackle those challenges, Panoramic Annular Localizer into which panoramic annular lens and robust deep image descriptors are incorporated is proposed in this paper. The panoramic annular images captured by the single camera are processed and fed into the NetVLAD network to form the active deep descriptor, and sequential matching is utilized to generate the localization result. The experiments carried on the public datasets and in the field illustrate the validation of the proposed system. | ['Huabing Li', 'Xiao Huang', 'Weijian Hu', 'Jian Bai', 'Shufei Lin', 'Kailun Yang', 'Ruiqi Cheng', 'Kaiwei Wang', 'Dongming Sun'] | 2019-05-14 | null | null | null | null | ['camera-localization', 'outdoor-localization'] | ['computer-vision', 'robots'] | [-2.01699361e-01 -9.16935205e-01 -2.43528441e-01 -3.95086974e-01
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f825036e-b779-4330-9e39-1b79fbe1056d | integrated-replay-spoofing-aware-text | 2006.05599 | null | https://arxiv.org/abs/2006.05599v2 | https://arxiv.org/pdf/2006.05599v2.pdf | Integrated Replay Spoofing-aware Text-independent Speaker Verification | A number of studies have successfully developed speaker verification or presentation attack detection systems. However, studies integrating the two tasks remain in the preliminary stages. In this paper, we propose two approaches for building an integrated system of speaker verification and presentation attack detection: an end-to-end monolithic approach and a back-end modular approach. The first approach simultaneously trains speaker identification, presentation attack detection, and the integrated system using multi-task learning using a common feature. However, through experiments, we hypothesize that the information required for performing speaker verification and presentation attack detection might differ because speaker verification systems try to remove device-specific information from speaker embeddings, while presentation attack detection systems exploit such information. Therefore, we propose a back-end modular approach using a separate deep neural network (DNN) for speaker verification and presentation attack detection. This approach has thee input components: two speaker embeddings (for enrollment and test each) and prediction of presentation attacks. Experiments are conducted using the ASVspoof 2017-v2 dataset, which includes official trials on the integration of speaker verification and presentation attack detection. The proposed back-end approach demonstrates a relative improvement of 21.77% in terms of the equal error rate for integrated trials compared to a conventional speaker verification system. | ['Ha-Jin Yu', 'Seung-bin Kim', 'Ju-ho Kim', 'Jee-weon Jung', 'Hye-jin Shim'] | 2020-06-10 | null | null | null | null | ['text-independent-speaker-verification'] | ['speech'] | [ 4.65447120e-02 -2.59790778e-01 1.03059866e-01 -5.53978145e-01
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dcfd9baf-78b3-46ec-ba4f-1d4a08b548f0 | stereovoxelnet-real-time-obstacle-detection | 2209.08459 | null | https://arxiv.org/abs/2209.08459v2 | https://arxiv.org/pdf/2209.08459v2.pdf | StereoVoxelNet: Real-Time Obstacle Detection Based on Occupancy Voxels from a Stereo Camera Using Deep Neural Networks | Obstacle detection is a safety-critical problem in robot navigation, where stereo matching is a popular vision-based approach. While deep neural networks have shown impressive results in computer vision, most of the previous obstacle detection works only leverage traditional stereo matching techniques to meet the computational constraints for real-time feedback. This paper proposes a computationally efficient method that employs a deep neural network to detect occupancy from stereo images directly. Instead of learning the point cloud correspondence from the stereo data, our approach extracts the compact obstacle distribution based on volumetric representations. In addition, we prune the computation of safety irrelevant spaces in a coarse-to-fine manner based on octrees generated by the decoder. As a result, we achieve real-time performance on the onboard computer (NVIDIA Jetson TX2). Our approach detects obstacles accurately in the range of 32 meters and achieves better IoU (Intersection over Union) and CD (Chamfer Distance) scores with only 2% of the computation cost of the state-of-the-art stereo model. Furthermore, we validate our method's robustness and real-world feasibility through autonomous navigation experiments with a real robot. Hence, our work contributes toward closing the gap between the stereo-based system in robot perception and state-of-the-art stereo models in computer vision. To counter the scarcity of high-quality real-world indoor stereo datasets, we collect a 1.36 hours stereo dataset with a mobile robot which is used to fine-tune our model. The dataset, the code, and further details including additional visualizations are available at https://lhy.xyz/stereovoxelnet | ['Taskin Padir', 'Yanzhi Wang', 'Huaizu Jiang', 'Neset Unver Akmandor', 'Zhengang Li', 'Hongyu Li'] | 2022-09-18 | null | null | null | null | ['stereo-matching-1'] | ['computer-vision'] | [ 1.26520827e-01 -9.41658467e-02 2.92399585e-01 -3.72173280e-01
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970f087f-0c13-4562-8b1a-e3562832b82b | adversarial-dropout-for-recurrent-neural | 1904.09816 | null | http://arxiv.org/abs/1904.09816v1 | http://arxiv.org/pdf/1904.09816v1.pdf | Adversarial Dropout for Recurrent Neural Networks | Successful application processing sequential data, such as text and speech,
requires an improved generalization performance of recurrent neural networks
(RNNs). Dropout techniques for RNNs were introduced to respond to these
demands, but we conjecture that the dropout on RNNs could have been improved by
adopting the adversarial concept. This paper investigates ways to improve the
dropout for RNNs by utilizing intentionally generated dropout masks.
Specifically, the guided dropout used in this research is called as adversarial
dropout, which adversarially disconnects neurons that are dominantly used to
predict correct targets over time. Our analysis showed that our regularizer,
which consists of a gap between the original and the reconfigured RNNs, was the
upper bound of the gap between the training and the inference phases of the
random dropout. We demonstrated that minimizing our regularizer improved the
effectiveness of the dropout for RNNs on sequential MNIST tasks,
semi-supervised text classification tasks, and language modeling tasks. | ['Il-Chul Moon', 'Mingi Ji', 'Kyungwoo Song', 'Wonsung Lee', 'Sungrae Park'] | 2019-04-22 | null | null | null | null | ['semi-supervised-text-classification-1'] | ['natural-language-processing'] | [ 4.32347506e-01 3.89204681e-01 1.09820075e-01 -5.36779642e-01
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9babd006-f099-4fb0-b2d6-c032eb3c5dfd | array-configuration-agnostic-personal-voice | 2304.08887 | null | https://arxiv.org/abs/2304.08887v1 | https://arxiv.org/pdf/2304.08887v1.pdf | Array Configuration-Agnostic Personal Voice Activity Detection Based on Spatial Coherence | Personal voice activity detection has received increased attention due to the growing popularity of personal mobile devices and smart speakers. PVAD is often an integral element to speech enhancement and recognition for these applications in which lightweight signal processing is only enabled for the target user. However, in real-world scenarios, the detection performance may degrade because of competing speakers, background noise, and reverberation. To address this problem, we proposed to use equivalent rectangular bandwidth ERB-scaled spatial coherence as the input feature to train an array configuration-agnostic PVAD network. Whereas the network model requires only 112k parameters, it exhibits excellent detection performance and robustness in adverse acoustic conditions. Notably, the proposed ARCA-PVAD system is scalable to array configurations. Experimental results have demonstrated the superior performance achieved by the proposed ARCA-PVAD system over a baseline in terms of the area under receiver operating characteristic curve and equal error rate. | ['Mingsian R. Bai', 'Yicheng Hsu'] | 2023-04-18 | null | null | null | null | ['activity-detection', 'speech-enhancement'] | ['computer-vision', 'speech'] | [ 3.29407662e-01 -1.94555566e-01 2.54745901e-01 -1.99824989e-01
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a49af599-ee55-4b14-bfa0-505fd6554a79 | hierarchical-attention-transfer-network-for | null | null | https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/viewPaper/16873 | https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16873/16149 | Hierarchical Attention Transfer Network for Cross-Domain Sentiment Classification | Cross-domain sentiment classification aims to leverage useful information in a source domain to help do sentiment classifi- cation in a target domain that has no or little supervised infor- mation. Existing cross-domain sentiment classification meth- ods cannot automatically capture non-pivots, i.e., the domain- specific sentiment words, and pivots, i.e., the domain-shared sentiment words, simultaneously. In order to solve this prob- lem, we propose a Hierarchical Attention Transfer Network (HATN) for cross-domain sentiment classification. The pro- posed HATN provides a hierarchical attention transfer mech- anism which can transfer attentions for emotions across do- mains by automatically capturing pivots and non-pivots. Be- sides, the hierarchy of the attention mechanism mirrors the hierarchical structure of documents, which can help locate the pivots and non-pivots better. The proposed HATN consists of two hierarchical attention networks, with one named P-net aiming to find the pivots and the other named NP-net align- ing the non-pivots by using the pivots as a bridge. Specif- ically, P-net firstly conducts individual attention learning to provide positive and negative pivots for NP-net. Then, P- net and NP-net conduct joint attention learning such that the HATN can simultaneously capture pivots and non-pivots and realize transferring attentions for emotions across domains. Experiments on the Amazon review dataset demonstrate the effectiveness of HATN. | ['Ying WEI', 'Zheng Li', 'Qiang Yang', 'Yu Zhang'] | 2018-04-26 | null | null | null | thirty-second-aaai-conference-on-artificial | ['cross-domain-text-classification'] | ['natural-language-processing'] | [-4.73376274e-01 -1.54922634e-01 -4.18859184e-01 -4.66352552e-01
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25410f91-b244-462a-a680-47965c6f2556 | in-context-learning-unlocked-for-diffusion | 2305.01115 | null | https://arxiv.org/abs/2305.01115v1 | https://arxiv.org/pdf/2305.01115v1.pdf | In-Context Learning Unlocked for Diffusion Models | We present Prompt Diffusion, a framework for enabling in-context learning in diffusion-based generative models. Given a pair of task-specific example images, such as depth from/to image and scribble from/to image, and a text guidance, our model automatically understands the underlying task and performs the same task on a new query image following the text guidance. To achieve this, we propose a vision-language prompt that can model a wide range of vision-language tasks and a diffusion model that takes it as input. The diffusion model is trained jointly over six different tasks using these prompts. The resulting Prompt Diffusion model is the first diffusion-based vision-language foundation model capable of in-context learning. It demonstrates high-quality in-context generation on the trained tasks and generalizes effectively to new, unseen vision tasks with their respective prompts. Our model also shows compelling text-guided image editing results. Our framework, with code publicly available at https://github.com/Zhendong-Wang/Prompt-Diffusion, aims to facilitate research into in-context learning for computer vision. | ['Mingyuan Zhou', 'Zhangyang Wang', 'Weizhu Chen', 'Pengcheng He', 'Yelong Shen', 'Yadong Lu', 'Yifan Jiang', 'Zhendong Wang'] | 2023-05-01 | null | null | null | null | ['text-guided-image-editing'] | ['computer-vision'] | [ 5.32282591e-01 8.96708295e-02 1.26141950e-01 -7.25643754e-01
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f05da29f-d5ea-4259-acb9-cee677ba24cf | street-view-change-detection-with | null | null | https://link.springer.com/article/10.1007/s10514-018-9734-5 | https://link.springer.com/article/10.1007/s10514-018-9734-5 | Street-view change detection with deconvolutional networks | We propose a system for performing structural change detection in street-view videos captured by a vehicle-mounted monocular camera over time. Our approach is motivated by the need for more frequent and efficient updates in the large-scale maps used in autonomous vehicle navigation. Our method chains a multi-sensor fusion SLAM and fast dense 3D reconstruction pipeline, which provide coarsely registered image pairs to a deep Deconvolutional Network (DN) for pixel-wise change detection. We investigate two DN architectures for change detection, the first one is based on the idea of stacking contraction and expansion blocks while the second one is based on the idea of Fully Convolutional Networks. To train and evaluate our networks we introduce a new urban change detection dataset which is an order of magnitude larger than existing datasets and contains challenging changes due to seasonal and lighting variations. Our method outperforms existing literature on this dataset, which we make available to the community, and an existing panoramic change detection dataset, demonstrating its wide applicability. | ['Riccardo Gherardi', 'Roberto Arroyo', 'Germán Ros', 'Simon Stent', 'Pablo F. Alcantarilla'] | 2018-05-15 | null | null | null | autonomous-robots-2018-5 | ['change-detection', 'change-detection-for-remote-sensing-images'] | ['computer-vision', 'miscellaneous'] | [ 3.52617055e-01 -4.42252755e-01 6.43335702e-03 -5.50741017e-01
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335b6b09-8e8d-49ae-8815-36b86c9fa9c4 | extract-and-edit-an-alternative-to-back | 1904.02331 | null | http://arxiv.org/abs/1904.02331v1 | http://arxiv.org/pdf/1904.02331v1.pdf | Extract and Edit: An Alternative to Back-Translation for Unsupervised Neural Machine Translation | The overreliance on large parallel corpora significantly limits the
applicability of machine translation systems to the majority of language pairs.
Back-translation has been dominantly used in previous approaches for
unsupervised neural machine translation, where pseudo sentence pairs are
generated to train the models with a reconstruction loss. However, the pseudo
sentences are usually of low quality as translation errors accumulate during
training. To avoid this fundamental issue, we propose an alternative but more
effective approach, extract-edit, to extract and then edit real sentences from
the target monolingual corpora. Furthermore, we introduce a comparative
translation loss to evaluate the translated target sentences and thus train the
unsupervised translation systems. Experiments show that the proposed approach
consistently outperforms the previous state-of-the-art unsupervised machine
translation systems across two benchmarks (English-French and English-German)
and two low-resource language pairs (English-Romanian and English-Russian) by
more than 2 (up to 3.63) BLEU points. | ['William Yang Wang', 'Xin Wang', 'Jiawei Wu'] | 2019-04-04 | extract-and-edit-an-alternative-to-back-1 | https://aclanthology.org/N19-1120 | https://aclanthology.org/N19-1120.pdf | naacl-2019-6 | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [ 5.37906945e-01 8.01247284e-02 -3.11058402e-01 -4.72431302e-01
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a252bdec-af09-4e05-a945-0cdd08b1ebf6 | stock-market-prediction-via-deep-learning | 2212.12717 | null | https://arxiv.org/abs/2212.12717v2 | https://arxiv.org/pdf/2212.12717v2.pdf | Stock Market Prediction via Deep Learning Techniques: A Survey | Existing surveys on stock market prediction often focus on traditional machine learning methods instead of deep learning methods. This motivates us to provide a structured and comprehensive overview of the research on stock market prediction. We present four elaborated subtasks of stock market prediction and propose a novel taxonomy to summarize the state-of-the-art models based on deep neural networks. In addition, we also provide detailed statistics on the datasets and evaluation metrics commonly used in the stock market. Finally, we point out several future directions by sharing some new perspectives on stock market prediction. | ['Javen Qinfeng Shi', 'Lingqiao Liu', 'Ehsan Abbasnejad', 'Qingsen Yan', 'Yanxi Liu', 'Haiyao Cao', 'Yang Jiao', 'Qingying Zhao', 'Jinan Zou'] | 2022-12-24 | null | null | null | null | ['stock-market-prediction'] | ['time-series'] | [-9.95818973e-01 -3.39012831e-01 -8.20327222e-01 -2.07276583e-01
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98715a15-8b11-4217-bdeb-ab3a5ab2d915 | density-based-clustering-for-3d-object | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Ahmed_Density-Based_Clustering_for_3D_Object_Detection_in_Point_Clouds_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Ahmed_Density-Based_Clustering_for_3D_Object_Detection_in_Point_Clouds_CVPR_2020_paper.pdf | Density-Based Clustering for 3D Object Detection in Point Clouds | Current 3D detection networks either rely on 2D object proposals or try to directly predict bounding box parameters from each point in a scene. While former methods are dependent on performance of 2D detectors, latter approaches are challenging due to the sparsity and occlusion in point clouds, making it difficult to regress accurate parameters. In this work, we introduce a novel approach for 3D object detection that is significant in two main aspects: a) cascaded modular approach that focuses the receptive field of each module on specific points in the point cloud, for improved feature learning and b) a class agnostic instance segmentation module that is initiated using unsupervised clustering. The objective of a cascaded approach is to sequentially minimize the number of points running through the network. While three different modules perform the tasks of background-foreground segmentation, class agnostic instance segmentation and object detection, through individually trained point based networks. We also evaluate bayesian uncertainty in modules, demonstrating the over all level of confidence in our prediction results. Performance of the network is evaluated on the SUN RGB-D benchmark dataset, that demonstrates an improvement as compared to state-of-the-art methods.
| [' Chee Meng Chew', 'Syeda Mariam Ahmed'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['foreground-segmentation'] | ['computer-vision'] | [ 1.25822529e-01 2.31408134e-01 1.91306490e-02 -4.16242123e-01
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