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ed32b489-c2b9-46ed-83d3-65420d134dbc | inter-database-validation-of-a-deep-learning | 2009.10365 | null | https://arxiv.org/abs/2009.10365v1 | https://arxiv.org/pdf/2009.10365v1.pdf | Inter-database validation of a deep learning approach for automatic sleep scoring | In this work we describe a new deep learning approach for automatic sleep staging, and carry out its validation by addressing its generalization capabilities on a wide range of sleep staging databases. Prediction capabilities are evaluated in the context of independent local and external generalization scenarios. Effectively, by comparing both procedures it is possible to better extrapolate the expected performance of the method on the general reference task of sleep staging, regardless of data from a specific database. In addition, we examine the suitability of a novel approach based on the use of an ensemble of individual local models and evaluate its impact on the resulting inter-database generalization performance. Validation results show good general performance, as compared to the expected levels of human expert agreement, as well as state-of-the-art automatic sleep staging approaches | ['Diego Alvarez-Estevez', 'Roselyne M. Rijsman'] | 2020-09-22 | null | null | null | null | ['sleep-staging'] | ['medical'] | [-2.58332640e-02 5.80320284e-02 -2.11450756e-01 -5.97503543e-01
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a6783449-5472-48ee-9a63-c23e709b2a50 | adversarially-robust-prototypical-few-shot | 2210.03429 | null | https://arxiv.org/abs/2210.03429v1 | https://arxiv.org/pdf/2210.03429v1.pdf | Adversarially Robust Prototypical Few-shot Segmentation with Neural-ODEs | Few-shot Learning (FSL) methods are being adopted in settings where data is not abundantly available. This is especially seen in medical domains where the annotations are expensive to obtain. Deep Neural Networks have been shown to be vulnerable to adversarial attacks. This is even more severe in the case of FSL due to the lack of a large number of training examples. In this paper, we provide a framework to make few-shot segmentation models adversarially robust in the medical domain where such attacks can severely impact the decisions made by clinicians who use them. We propose a novel robust few-shot segmentation framework, Prototypical Neural Ordinary Differential Equation (PNODE), that provides defense against gradient-based adversarial attacks. We show that our framework is more robust compared to traditional adversarial defense mechanisms such as adversarial training. Adversarial training involves increased training time and shows robustness to limited types of attacks depending on the type of adversarial examples seen during training. Our proposed framework generalises well to common adversarial attacks like FGSM, PGD and SMIA while having the model parameters comparable to the existing few-shot segmentation models. We show the effectiveness of our proposed approach on three publicly available multi-organ segmentation datasets in both in-domain and cross-domain settings by attacking the support and query sets without the need for ad-hoc adversarial training. | ['Brejesh lall', 'Tanuj Sur', 'Mustafa Chasmai', 'Aleti Vardhan', 'Prashant Pandey'] | 2022-10-07 | null | null | null | null | ['adversarial-defense'] | ['adversarial'] | [ 4.46609765e-01 3.42116594e-01 3.29531014e-01 -1.80361882e-01
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68f39507-469a-44b3-a38b-10f196211a1f | sequential-transformer-for-end-to-end-person | 2211.04323 | null | https://arxiv.org/abs/2211.04323v2 | https://arxiv.org/pdf/2211.04323v2.pdf | Sequential Transformer for End-to-End Person Search | Person Search aims to simultaneously localize and recognize a target person from realistic and uncropped gallery images. One major challenge of person search comes from the contradictory goals of the two sub-tasks, i.e., person detection focuses on finding the commonness of all persons so as to distinguish persons from the background, while person re-identification (re-ID) focuses on the differences among different persons. In this paper, we propose a novel Sequential Transformer (SeqTR) for end-to-end person search to deal with this challenge. Our SeqTR contains a detection transformer and a novel re-ID transformer that sequentially addresses detection and re-ID tasks. The re-ID transformer comprises the self-attention layer that utilizes contextual information and the cross-attention layer that learns local fine-grained discriminative features of the human body. Moreover, the re-ID transformer is shared and supervised by multi-scale features to improve the robustness of learned person representations. Extensive experiments on two widely-used person search benchmarks, CUHK-SYSU and PRW, show that our proposed SeqTR not only outperforms all existing person search methods with a 59.3% mAP on PRW but also achieves comparable performance to the state-of-the-art results with an mAP of 94.8% on CUHK-SYSU. | ['Jinhua Xu', 'Long Chen'] | 2022-11-06 | null | null | null | null | ['person-search'] | ['computer-vision'] | [-2.06907883e-01 -4.51593965e-01 4.47327234e-02 -1.03198282e-01
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aa3b3f97-933e-4c2a-830c-f2fb581e7711 | improving-temporal-relation-extraction-with-a | 1804.06020 | null | http://arxiv.org/abs/1804.06020v1 | http://arxiv.org/pdf/1804.06020v1.pdf | Improving Temporal Relation Extraction with a Globally Acquired Statistical Resource | Extracting temporal relations (before, after, overlapping, etc.) is a key
aspect of understanding events described in natural language. We argue that
this task would gain from the availability of a resource that provides prior
knowledge in the form of the temporal order that events usually follow. This
paper develops such a resource -- a probabilistic knowledge base acquired in
the news domain -- by extracting temporal relations between events from the New
York Times (NYT) articles over a 20-year span (1987--2007). We show that
existing temporal extraction systems can be improved via this resource. As a
byproduct, we also show that interesting statistics can be retrieved from this
resource, which can potentially benefit other time-aware tasks. The proposed
system and resource are both publicly available. | ['Qiang Ning', 'Hao Wu', 'Dan Roth', 'Haoruo Peng'] | 2018-04-17 | improving-temporal-relation-extraction-with-a-1 | https://aclanthology.org/N18-1077 | https://aclanthology.org/N18-1077.pdf | naacl-2018-6 | ['temporal-relation-extraction'] | ['natural-language-processing'] | [-1.73671797e-01 2.32912868e-01 -4.81805742e-01 -6.01061106e-01
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da03b5af-cf57-43ac-9ab5-f6e638ef8f66 | on-the-cross-lingual-transferability-of-2 | null | null | https://aclanthology.org/2021.mrl-1.10 | https://aclanthology.org/2021.mrl-1.10.pdf | On the Cross-lingual Transferability of Contextualized Sense Embeddings | In this paper we analyze the extent to which contextualized sense embeddings, i.e., sense embeddings that are computed based on contextualized word embeddings, are transferable across languages.To this end, we compiled a unified cross-lingual benchmark for Word Sense Disambiguation. We then propose two simple strategies to transfer sense-specific knowledge across languages and test them on the benchmark.Experimental results show that this contextualized knowledge can be effectively transferred to similar languages through pre-trained multilingual language models, to the extent that they can out-perform monolingual representations learnednfrom existing language-specific data. | ['Mohammad Taher Pilehvar', 'Jose Camacho-Collados', 'Daniel Loureiro', 'Kiamehr Rezaee'] | null | null | null | null | emnlp-mrl-2021-11 | ['word-sense-disambiguation'] | ['natural-language-processing'] | [-1.35908052e-01 -2.61281058e-02 -5.56988776e-01 -3.34668696e-01
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fe9e2576-6146-40ad-aa4e-4ea3cc7c1310 | jointly-learning-sentence-embeddings-and | 1705.09189 | null | http://arxiv.org/abs/1705.09189v1 | http://arxiv.org/pdf/1705.09189v1.pdf | Jointly Learning Sentence Embeddings and Syntax with Unsupervised Tree-LSTMs | We introduce a neural network that represents sentences by composing their
words according to induced binary parse trees. We use Tree-LSTM as our
composition function, applied along a tree structure found by a fully
differentiable natural language chart parser. Our model simultaneously
optimises both the composition function and the parser, thus eliminating the
need for externally-provided parse trees which are normally required for
Tree-LSTM. It can therefore be seen as a tree-based RNN that is unsupervised
with respect to the parse trees. As it is fully differentiable, our model is
easily trained with an off-the-shelf gradient descent method and
backpropagation. We demonstrate that it achieves better performance compared to
various supervised Tree-LSTM architectures on a textual entailment task and a
reverse dictionary task. | ['Stephen Clark', 'Jean Maillard', 'Dani Yogatama'] | 2017-05-25 | jointly-learning-sentence-embeddings-and-1 | https://openreview.net/forum?id=BJMuY-gRW | https://openreview.net/pdf?id=BJMuY-gRW | iclr-2018-1 | ['reverse-dictionary'] | ['natural-language-processing'] | [ 4.92376655e-01 5.83868861e-01 -1.26197666e-01 -6.42111242e-01
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4442e09a-7cde-4e53-8792-7fbc333b3bbf | harnessing-label-semantics-to-extract-higher | 2212.01685 | null | https://arxiv.org/abs/2212.01685v1 | https://arxiv.org/pdf/2212.01685v1.pdf | Harnessing label semantics to extract higher performance under noisy label for Company to Industry matching | Assigning appropriate industry tag(s) to a company is a critical task in a financial institution as it impacts various financial machineries. Yet, it remains a complex task. Typically, such industry tags are to be assigned by Subject Matter Experts (SME) after evaluating company business lines against the industry definitions. It becomes even more challenging as companies continue to add new businesses and newer industry definitions are formed. Given the periodicity of the task it is reasonable to assume that an Artificial Intelligent (AI) agent could be developed to carry it out in an efficient manner. While this is an exciting prospect, the challenges appear from the need of historical patterns of such tag assignments (or Labeling). Labeling is often considered the most expensive task in Machine Learning (ML) due its dependency on SMEs and manual efforts. Therefore, often, in enterprise set up, an ML project encounters noisy and dependent labels. Such labels create technical hindrances for ML Models to produce robust tag assignments. We propose an ML pipeline which uses semantic similarity matching as an alternative to multi label text classification, while making use of a Label Similarity Matrix and a minimum labeling strategy. We demonstrate this pipeline achieves significant improvements over the noise and exhibit robust predictive capabilities. | ['Abhishek Mitra', 'Apoorva Jaiswal'] | 2022-12-03 | null | null | null | null | ['multi-label-text-classification', 'multi-label-text-classification'] | ['methodology', 'natural-language-processing'] | [ 4.23504770e-01 6.98627904e-02 -6.00550957e-02 -4.67444181e-01
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7ffe9c94-80f2-4aa2-8d50-4331ba087814 | ms-a-new-exact-algorithm-for-multi-agent | 2103.09979 | null | https://arxiv.org/abs/2103.09979v1 | https://arxiv.org/pdf/2103.09979v1.pdf | MS*: A New Exact Algorithm for Multi-agent Simultaneous Multi-goal Sequencing and Path Finding | In multi-agent applications such as surveillance and logistics, fleets of mobile agents are often expected to coordinate and safely visit a large number of goal locations as efficiently as possible. The multi-agent planning problem in these applications involves allocating and sequencing goals for each agent while simultaneously producing conflict-free paths for the agents. In this article, we introduce a new algorithm called MS* which computes an optimal solution for this multi-agent problem by fusing and advancing state of the art solvers for multi-agent path finding (MAPF) and multiple travelling salesman problem (mTSP). MS* leverages our prior subdimensional expansion approach for MAPF and embeds the mTSP solvers to optimally allocate and sequence goals for agents. Numerical results show that our new algorithm can solve the multi-agent problem with 20 agents and 50 goals in a minute of CPU time on a standard laptop. | ['Howie Choset', 'Sivakumar Rathinam', 'Zhongqiang Ren'] | 2021-03-18 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [ 4.71713543e-02 3.49339873e-01 2.60038488e-02 4.45440076e-02
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dc2f5f31-0327-46dd-8308-d3609551242a | tinc-tree-structured-implicit-neural | 2211.06689 | null | https://arxiv.org/abs/2211.06689v4 | https://arxiv.org/pdf/2211.06689v4.pdf | TINC: Tree-structured Implicit Neural Compression | Implicit neural representation (INR) can describe the target scenes with high fidelity using a small number of parameters, and is emerging as a promising data compression technique. However, limited spectrum coverage is intrinsic to INR, and it is non-trivial to remove redundancy in diverse complex data effectively. Preliminary studies can only exploit either global or local correlation in the target data and thus of limited performance. In this paper, we propose a Tree-structured Implicit Neural Compression (TINC) to conduct compact representation for local regions and extract the shared features of these local representations in a hierarchical manner. Specifically, we use Multi-Layer Perceptrons (MLPs) to fit the partitioned local regions, and these MLPs are organized in tree structure to share parameters according to the spatial distance. The parameter sharing scheme not only ensures the continuity between adjacent regions, but also jointly removes the local and non-local redundancy. Extensive experiments show that TINC improves the compression fidelity of INR, and has shown impressive compression capabilities over commercial tools and other deep learning based methods. Besides, the approach is of high flexibility and can be tailored for different data and parameter settings. The source code can be found at https://github.com/RichealYoung/TINC . | ['Qionghai Dai', 'Jinli Suo', 'Yuxiao Cheng', 'Tingxiong Xiao', 'Runzhao Yang'] | 2022-11-12 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Yang_TINC_Tree-Structured_Implicit_Neural_Compression_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Yang_TINC_Tree-Structured_Implicit_Neural_Compression_CVPR_2023_paper.pdf | cvpr-2023-1 | ['data-compression'] | ['time-series'] | [ 3.15380663e-01 -2.32178017e-01 -4.65557724e-01 -1.60300449e-01
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3402048c-2bc0-40ef-b6f4-15098cf49f9e | bi-directional-feature-reconstruction-network | 2211.17161 | null | https://arxiv.org/abs/2211.17161v2 | https://arxiv.org/pdf/2211.17161v2.pdf | Bi-directional Feature Reconstruction Network for Fine-Grained Few-Shot Image Classification | The main challenge for fine-grained few-shot image classification is to learn feature representations with higher inter-class and lower intra-class variations, with a mere few labelled samples. Conventional few-shot learning methods however cannot be naively adopted for this fine-grained setting -- a quick pilot study reveals that they in fact push for the opposite (i.e., lower inter-class variations and higher intra-class variations). To alleviate this problem, prior works predominately use a support set to reconstruct the query image and then utilize metric learning to determine its category. Upon careful inspection, we further reveal that such unidirectional reconstruction methods only help to increase inter-class variations and are not effective in tackling intra-class variations. In this paper, we for the first time introduce a bi-reconstruction mechanism that can simultaneously accommodate for inter-class and intra-class variations. In addition to using the support set to reconstruct the query set for increasing inter-class variations, we further use the query set to reconstruct the support set for reducing intra-class variations. This design effectively helps the model to explore more subtle and discriminative features which is key for the fine-grained problem in hand. Furthermore, we also construct a self-reconstruction module to work alongside the bi-directional module to make the features even more discriminative. Experimental results on three widely used fine-grained image classification datasets consistently show considerable improvements compared with other methods. Codes are available at: https://github.com/PRIS-CV/Bi-FRN. | ['Yi-Zhe Song', 'Jun Guo', 'Jie Cao', 'Zhanyu Ma', 'Xiaoxu Li', 'Aneeshan Sain', 'Dongliang Chang', 'Jijie Wu'] | 2022-11-30 | null | null | null | null | ['few-shot-image-classification', 'fine-grained-image-classification'] | ['computer-vision', 'computer-vision'] | [ 2.21158400e-01 -2.82207310e-01 -4.77538198e-01 -4.99295801e-01
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3e964aa0-74a6-451d-a979-e7a95f7133dc | self-generated-defocus-blur-detection-via | null | null | http://openaccess.thecvf.com//content/CVPR2021/html/Zhao_Self-Generated_Defocus_Blur_Detection_via_Dual_Adversarial_Discriminators_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Zhao_Self-Generated_Defocus_Blur_Detection_via_Dual_Adversarial_Discriminators_CVPR_2021_paper.pdf | Self-Generated Defocus Blur Detection via Dual Adversarial Discriminators | Although existing fully-supervised defocus blur detection (DBD) models significantly improve performance, training such deep models requires abundant pixel-level manual annotation, which is highly time-consuming and error-prone. Addressing this issue, this paper makes an effort to train a deep DBD model without using any pixel-level annotation. The core insight is that a defocus blur region/focused clear area can be arbitrarily pasted to a given realistic full blurred image/full clear image without affecting the judgment of the full blurred image/full clear image. Specifically, we train a generator G in an adversarial manner against dual discriminators Dc and Db. G learns to produce a DBD mask that generates a composite clear image and a composite blurred image through copying the focused area and unfocused region from corresponding source image to another full clear image and full blurred image. Then, Dc and Db can not distinguish them from realistic full clear image and full blurred image simultaneously, achieving a self-generated DBD by an implicit manner to define what a defocus blur area is. Besides, we propose a bilateral triplet-excavating constraint to avoid the degenerate problem caused by the case one discriminator defeats the other one. Comprehensive experiments on two widely-used DBD datasets demonstrate the superiority of the proposed approach. Source codes are available at: https://github.com/shangcai1/SG. | ['Huchuan Lu', 'Cai Shang', 'Wenda Zhao'] | 2021-06-19 | null | null | null | cvpr-2021-1 | ['defocus-blur-detection'] | ['computer-vision'] | [ 4.96081859e-01 -1.19120106e-01 2.16221139e-01 -4.37172264e-01
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d84b9b73-dfbe-413b-8d4e-480c0cc9cc01 | explicit-homography-estimation-improves-1 | 2101.04713 | null | https://arxiv.org/abs/2101.04713v1 | https://arxiv.org/pdf/2101.04713v1.pdf | Explicit homography estimation improves contrastive self-supervised learning | The typical contrastive self-supervised algorithm uses a similarity measure in latent space as the supervision signal by contrasting positive and negative images directly or indirectly. Although the utility of self-supervised algorithms has improved recently, there are still bottlenecks hindering their widespread use, such as the compute needed. In this paper, we propose a module that serves as an additional objective in the self-supervised contrastive learning paradigm. We show how the inclusion of this module to regress the parameters of an affine transformation or homography, in addition to the original contrastive objective, improves both performance and learning speed. Importantly, we ensure that this module does not enforce invariance to the various components of the affine transform, as this is not always ideal. We demonstrate the effectiveness of the additional objective on two recent, popular self-supervised algorithms. We perform an extensive experimental analysis of the proposed method and show an improvement in performance for all considered datasets. Further, we find that although both the general homography and affine transformation are sufficient to improve performance and convergence, the affine transformation performs better in all cases. | ['Richard Klein', 'David Torpey'] | 2021-01-12 | explicit-homography-estimation-improves | https://openreview.net/forum?id=bWqodw-mFi1 | https://openreview.net/pdf?id=bWqodw-mFi1 | null | ['homography-estimation'] | ['computer-vision'] | [ 3.68007541e-01 6.11543097e-02 -3.01803142e-01 -5.10687351e-01
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42042e4f-f64a-45a2-ac90-39a2ffdef576 | span-selective-linear-attention-transformers | 2306.09340 | null | https://arxiv.org/abs/2306.09340v1 | https://arxiv.org/pdf/2306.09340v1.pdf | Span-Selective Linear Attention Transformers for Effective and Robust Schema-Guided Dialogue State Tracking | In schema-guided dialogue state tracking models estimate the current state of a conversation using natural language descriptions of the service schema for generalization to unseen services. Prior generative approaches which decode slot values sequentially do not generalize well to variations in schema, while discriminative approaches separately encode history and schema and fail to account for inter-slot and intent-slot dependencies. We introduce SPLAT, a novel architecture which achieves better generalization and efficiency than prior approaches by constraining outputs to a limited prediction space. At the same time, our model allows for rich attention among descriptions and history while keeping computation costs constrained by incorporating linear-time attention. We demonstrate the effectiveness of our model on the Schema-Guided Dialogue (SGD) and MultiWOZ datasets. Our approach significantly improves upon existing models achieving 85.3 JGA on the SGD dataset. Further, we show increased robustness on the SGD-X benchmark: our model outperforms the more than 30$\times$ larger D3ST-XXL model by 5.0 points. | ['Haejun Lee', 'Björn Bebensee'] | 2023-06-15 | null | null | null | null | ['dialogue-state-tracking'] | ['natural-language-processing'] | [ 7.99771957e-03 8.16892743e-01 -4.00060624e-01 -9.63954568e-01
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39f3d690-0046-468d-a98e-6f9976000949 | monocular-direct-sparse-localization-in-a | 2002.09923 | null | https://arxiv.org/abs/2002.09923v1 | https://arxiv.org/pdf/2002.09923v1.pdf | Monocular Direct Sparse Localization in a Prior 3D Surfel Map | In this paper, we introduce an approach to tracking the pose of a monocular camera in a prior surfel map. By rendering vertex and normal maps from the prior surfel map, the global planar information for the sparse tracked points in the image frame is obtained. The tracked points with and without the global planar information involve both global and local constraints of frames to the system. Our approach formulates all constraints in the form of direct photometric errors within a local window of the frames. The final optimization utilizes these constraints to provide the accurate estimation of global 6-DoF camera poses with the absolute scale. The extensive simulation and real-world experiments demonstrate that our monocular method can provide accurate camera localization results under various conditions. | ['Huaiyang Huang', 'Haoyang Ye', 'Ming Liu'] | 2020-02-23 | null | null | null | null | ['camera-localization'] | ['computer-vision'] | [-2.39325896e-01 -5.52238464e-01 -2.56761104e-01 -8.46717134e-02
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aa56889b-614e-4e85-aca9-852ecbb4c04b | learning-transferable-domain-priors-for-safe | 1909.04307 | null | https://arxiv.org/abs/1909.04307v5 | https://arxiv.org/pdf/1909.04307v5.pdf | Learning Transferable Domain Priors for Safe Exploration in Reinforcement Learning | Prior access to domain knowledge could significantly improve the performance of a reinforcement learning agent. In particular, it could help agents avoid potentially catastrophic exploratory actions, which would otherwise have to be experienced during learning. In this work, we identify consistently undesirable actions in a set of previously learned tasks, and use pseudo-rewards associated with them to learn a prior policy. In addition to enabling safer exploratory behaviors in subsequent tasks in the domain, we show that these priors are transferable to similar environments, and can be learned off-policy and in parallel with the learning of other tasks in the domain. We compare our approach to established, state-of-the-art algorithms in both discrete as well as continuous environments, and demonstrate that it exhibits a safer exploratory behavior while learning to perform arbitrary tasks in the domain. We also present a theoretical analysis to support these results, and briefly discuss the implications and some alternative formulations of this approach, which could also be useful in certain scenarios. | ['Truyen Tran', 'Thommen George Karimpanal', 'Sunil Gupta', 'Svetha Venkatesh', 'Santu Rana'] | 2019-09-10 | null | null | null | null | ['safe-exploration'] | ['robots'] | [ 3.06030065e-01 3.29852968e-01 -2.84503531e-02 -4.11389023e-02
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a49bd9b7-d78f-4d6a-bc91-86d66521170b | artelingo-a-million-emotion-annotations-of | 2211.10780 | null | https://arxiv.org/abs/2211.10780v1 | https://arxiv.org/pdf/2211.10780v1.pdf | ArtELingo: A Million Emotion Annotations of WikiArt with Emphasis on Diversity over Language and Culture | This paper introduces ArtELingo, a new benchmark and dataset, designed to encourage work on diversity across languages and cultures. Following ArtEmis, a collection of 80k artworks from WikiArt with 0.45M emotion labels and English-only captions, ArtELingo adds another 0.79M annotations in Arabic and Chinese, plus 4.8K in Spanish to evaluate "cultural-transfer" performance. More than 51K artworks have 5 annotations or more in 3 languages. This diversity makes it possible to study similarities and differences across languages and cultures. Further, we investigate captioning tasks, and find diversity improves the performance of baseline models. ArtELingo is publicly available at https://www.artelingo.org/ with standard splits and baseline models. We hope our work will help ease future research on multilinguality and culturally-aware AI. | ['Mohamed Elhoseiny', 'Kenneth Ward Church', 'Xiangliang Zhang', 'Feifan Li', 'Shyma Alhuwaider', 'Mohamed Abdelfattah', 'Youssef Mohamed'] | 2022-11-19 | null | null | null | null | ['culture'] | ['speech'] | [-3.27342063e-01 -5.07747009e-02 -3.35792333e-01 -4.06117857e-01
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bd796d94-1701-4d52-96fe-8cbaeb3f651c | score-based-diffusion-models-for-bayesian | 2305.16482 | null | https://arxiv.org/abs/2305.16482v1 | https://arxiv.org/pdf/2305.16482v1.pdf | Score-based Diffusion Models for Bayesian Image Reconstruction | This paper explores the use of score-based diffusion models for Bayesian image reconstruction. Diffusion models are an efficient tool for generative modeling. Diffusion models can also be used for solving image reconstruction problems. We present a simple and flexible algorithm for training a diffusion model and using it for maximum a posteriori reconstruction, minimum mean square error reconstruction, and posterior sampling. We present experiments on both a linear and a nonlinear reconstruction problem that highlight the strengths and limitations of the approach. | ['Marc L. Klasky', 'Jong Chul Ye', 'Hyungjin Chung', 'Michael T. McCann'] | 2023-05-25 | null | null | null | null | ['image-reconstruction'] | ['computer-vision'] | [ 2.23532319e-01 9.61957350e-02 -2.47971117e-01 -3.99710655e-01
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2c469464-f972-48e8-b320-0b0707452f04 | self-supervised-multi-view-learning-via-auto-1 | 2103.00787 | null | https://arxiv.org/abs/2103.00787v1 | https://arxiv.org/pdf/2103.00787v1.pdf | Self-Supervised Multi-View Learning via Auto-Encoding 3D Transformations | 3D object representation learning is a fundamental challenge in computer vision to infer about the 3D world. Recent advances in deep learning have shown their efficiency in 3D object recognition, among which view-based methods have performed best so far. However, feature learning of multiple views in existing methods is mostly performed in a supervised fashion, which often requires a large amount of data labels with high costs. In contrast, self-supervised learning aims to learn multi-view feature representations without involving labeled data. To this end, we propose a novel self-supervised paradigm to learn Multi-View Transformation Equivariant Representations (MV-TER), exploring the equivariant transformations of a 3D object and its projected multiple views. Specifically, we perform a 3D transformation on a 3D object, and obtain multiple views before and after the transformation via projection. Then, we self-train a representation to capture the intrinsic 3D object representation by decoding 3D transformation parameters from the fused feature representations of multiple views before and after the transformation. Experimental results demonstrate that the proposed MV-TER significantly outperforms the state-of-the-art view-based approaches in 3D object classification and retrieval tasks, and show the generalization to real-world datasets. | ['Guo-Jun Qi', 'Wei Hu', 'Xiang Gao'] | 2021-03-01 | self-supervised-multi-view-learning-via-auto | https://openreview.net/forum?id=0fqoSxXBwI6 | https://openreview.net/pdf?id=0fqoSxXBwI6 | null | ['3d-object-classification', '3d-object-recognition', 'multi-view-learning'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-9.23745893e-03 -3.92236501e-01 -7.15364590e-02 -6.35092616e-01
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0c5d6ba9-9df5-4813-93b2-ff92858f407f | learning-open-world-object-proposals-without | 2108.06753 | null | https://arxiv.org/abs/2108.06753v1 | https://arxiv.org/pdf/2108.06753v1.pdf | Learning Open-World Object Proposals without Learning to Classify | Object proposals have become an integral preprocessing steps of many vision pipelines including object detection, weakly supervised detection, object discovery, tracking, etc. Compared to the learning-free methods, learning-based proposals have become popular recently due to the growing interest in object detection. The common paradigm is to learn object proposals from data labeled with a set of object regions and their corresponding categories. However, this approach often struggles with novel objects in the open world that are absent in the training set. In this paper, we identify that the problem is that the binary classifiers in existing proposal methods tend to overfit to the training categories. Therefore, we propose a classification-free Object Localization Network (OLN) which estimates the objectness of each region purely by how well the location and shape of a region overlap with any ground-truth object (e.g., centerness and IoU). This simple strategy learns generalizable objectness and outperforms existing proposals on cross-category generalization on COCO, as well as cross-dataset evaluation on RoboNet, Object365, and EpicKitchens. Finally, we demonstrate the merit of OLN for long-tail object detection on large vocabulary dataset, LVIS, where we notice clear improvement in rare and common categories. | ['Weicheng Kuo', 'In So Kweon', 'Anelia Angelova', 'Tsung-Yi Lin', 'Dahun Kim'] | 2021-08-15 | null | null | null | null | ['zero-shot-object-detection', 'open-world-object-detection'] | ['computer-vision', 'computer-vision'] | [-1.77005768e-01 -2.17169255e-01 -2.16436639e-01 -5.18394291e-01
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38cfd427-565b-4b80-8742-7b51c60cbed7 | a-subsequence-interleaving-model-for | 1602.05012 | null | http://arxiv.org/abs/1602.05012v2 | http://arxiv.org/pdf/1602.05012v2.pdf | A Subsequence Interleaving Model for Sequential Pattern Mining | Recent sequential pattern mining methods have used the minimum description
length (MDL) principle to define an encoding scheme which describes an
algorithm for mining the most compressing patterns in a database. We present a
novel subsequence interleaving model based on a probabilistic model of the
sequence database, which allows us to search for the most compressing set of
patterns without designing a specific encoding scheme. Our proposed algorithm
is able to efficiently mine the most relevant sequential patterns and rank them
using an associated measure of interestingness. The efficient inference in our
model is a direct result of our use of a structural expectation-maximization
framework, in which the expectation-step takes the form of a submodular
optimization problem subject to a coverage constraint. We show on both
synthetic and real world datasets that our model mines a set of sequential
patterns with low spuriousness and redundancy, high interpretability and
usefulness in real-world applications. Furthermore, we demonstrate that the
quality of the patterns from our approach is comparable to, if not better than,
existing state of the art sequential pattern mining algorithms. | ['Jaroslav Fowkes', 'Charles Sutton'] | 2016-02-16 | null | null | null | null | ['sequential-pattern-mining'] | ['natural-language-processing'] | [ 6.83811426e-01 3.56409252e-02 -4.45689738e-01 -2.98277020e-01
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1d739aa6-a4a4-4717-8e77-353f611d61ae | the-hybridization-of-branch-and-bound-with | 2212.04624 | null | https://arxiv.org/abs/2212.04624v1 | https://arxiv.org/pdf/2212.04624v1.pdf | The Hybridization of Branch and Bound with Metaheuristics for Nonconvex Multiobjective Optimization | A hybrid framework combining the branch and bound method with multiobjective evolutionary algorithms is proposed for nonconvex multiobjective optimization. The hybridization exploits the complementary character of the two optimization strategies. A multiobjective evolutionary algorithm is intended for inducing tight lower and upper bounds during the branch and bound procedure. Tight bounds such as the ones derived in this way can reduce the number of subproblems that have to be solved. The branch and bound method guarantees the global convergence of the framework and improves the search capability of the multiobjective evolutionary algorithm. An implementation of the hybrid framework considering NSGA-II and MOEA/D-DE as multiobjective evolutionary algorithms is presented. Numerical experiments verify the hybrid algorithms benefit from synergy of the branch and bound method and multiobjective evolutionary algorithms. | ['Xin-min Yang', 'Wei-tian Wu'] | 2022-12-09 | null | null | null | null | ['multiobjective-optimization'] | ['methodology'] | [-9.72609222e-02 -1.26085982e-01 -1.07286036e-01 -9.54419523e-02
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a29244bf-5922-44e0-a90a-ec046095db14 | beyond-reward-offline-preference-guided | 2305.16217 | null | https://arxiv.org/abs/2305.16217v2 | https://arxiv.org/pdf/2305.16217v2.pdf | Beyond Reward: Offline Preference-guided Policy Optimization | This study focuses on the topic of offline preference-based reinforcement learning (PbRL), a variant of conventional reinforcement learning that dispenses with the need for online interaction or specification of reward functions. Instead, the agent is provided with fixed offline trajectories and human preferences between pairs of trajectories to extract the dynamics and task information, respectively. Since the dynamics and task information are orthogonal, a naive approach would involve using preference-based reward learning followed by an off-the-shelf offline RL algorithm. However, this requires the separate learning of a scalar reward function, which is assumed to be an information bottleneck of the learning process. To address this issue, we propose the offline preference-guided policy optimization (OPPO) paradigm, which models offline trajectories and preferences in a one-step process, eliminating the need for separately learning a reward function. OPPO achieves this by introducing an offline hindsight information matching objective for optimizing a contextual policy and a preference modeling objective for finding the optimal context. OPPO further integrates a well-performing decision policy by optimizing the two objectives iteratively. Our empirical results demonstrate that OPPO effectively models offline preferences and outperforms prior competing baselines, including offline RL algorithms performed over either true or pseudo reward function specifications. Our code is available on the project website: https://sites.google.com/view/oppo-icml-2023 . | ['Donglin Wang', 'Li He', 'Jinxin Liu', 'Diyuan Shi', 'Yachen Kang'] | 2023-05-25 | null | null | null | null | ['offline-rl'] | ['playing-games'] | [-2.15712994e-01 -3.25116180e-02 -5.19064426e-01 -2.26454258e-01
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d73c24db-0b4f-4f0b-8666-f84dc722c955 | magic3d-high-resolution-text-to-3d-content | 2211.10440 | null | https://arxiv.org/abs/2211.10440v2 | https://arxiv.org/pdf/2211.10440v2.pdf | Magic3D: High-Resolution Text-to-3D Content Creation | DreamFusion has recently demonstrated the utility of a pre-trained text-to-image diffusion model to optimize Neural Radiance Fields (NeRF), achieving remarkable text-to-3D synthesis results. However, the method has two inherent limitations: (a) extremely slow optimization of NeRF and (b) low-resolution image space supervision on NeRF, leading to low-quality 3D models with a long processing time. In this paper, we address these limitations by utilizing a two-stage optimization framework. First, we obtain a coarse model using a low-resolution diffusion prior and accelerate with a sparse 3D hash grid structure. Using the coarse representation as the initialization, we further optimize a textured 3D mesh model with an efficient differentiable renderer interacting with a high-resolution latent diffusion model. Our method, dubbed Magic3D, can create high quality 3D mesh models in 40 minutes, which is 2x faster than DreamFusion (reportedly taking 1.5 hours on average), while also achieving higher resolution. User studies show 61.7% raters to prefer our approach over DreamFusion. Together with the image-conditioned generation capabilities, we provide users with new ways to control 3D synthesis, opening up new avenues to various creative applications. | ['Tsung-Yi Lin', 'Ming-Yu Liu', 'Sanja Fidler', 'Karsten Kreis', 'Xun Huang', 'Xiaohui Zeng', 'Towaki Takikawa', 'Luming Tang', 'Jun Gao', 'Chen-Hsuan Lin'] | 2022-11-18 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Lin_Magic3D_High-Resolution_Text-to-3D_Content_Creation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Lin_Magic3D_High-Resolution_Text-to-3D_Content_Creation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['text-to-3d'] | ['computer-vision'] | [ 5.19143604e-02 2.14962177e-02 1.26605138e-01 -2.30955571e-01
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9d95b0ae-bdf8-4929-9c64-2f0dc2c81c0e | spatiotemporal-cnns-for-pornography-detection | 1810.10519 | null | http://arxiv.org/abs/1810.10519v1 | http://arxiv.org/pdf/1810.10519v1.pdf | Spatiotemporal CNNs for Pornography Detection in Videos | With the increasing use of social networks and mobile devices, the number of
videos posted on the Internet is growing exponentially. Among the inappropriate
contents published on the Internet, pornography is one of the most worrying as
it can be accessed by teens and children. Two spatiotemporal CNNs, VGG-C3D CNN
and ResNet R(2+1)D CNN, were assessed for pornography detection in videos in
the present study. Experimental results using the Pornography-800 dataset
showed that these spatiotemporal CNNs performed better than some
state-of-the-art methods based on bag of visual words and are competitive with
other CNN-based approaches, reaching accuracy of 95.1%. | ['Aparecido Nilceu Marana', 'Murilo Varges da Silva'] | 2018-10-24 | null | null | null | null | ['pornography-detection'] | ['computer-vision'] | [-4.73051310e-01 -1.37979805e-01 -5.69550157e-01 1.93770900e-01
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22ae7fd2-453e-4168-aaed-dd3647c5f0c2 | generalized-radio-environment-monitoring-for | 2008.06203 | null | https://arxiv.org/abs/2008.06203v2 | https://arxiv.org/pdf/2008.06203v2.pdf | Generalized Radio Environment Monitoring for Next Generation Wireless Networks | Enabling technologies of 5G and beyond wireless communication networks, such as millimeter-wave communication, beamforming, and multiple-input multiple-output (MIMO) antenna systems, are becoming increasingly dependent on accurate information of the physical environment for optimized communication performance. The acquisition of this information is a significant challenge, which can be eased by knowledge of the radio environment. Radio environment mapping (REM) has long been considered an enabler of cognitive radios (CRs). However, the limitations of radios effectively confined the application of REM to spectrum sensing and interference mapping. With the advent of more capable software-defined radios and advanced networks, the idea of a truly intelligent communication system empowered by enhanced REM can be realized. To this effect, we propose the generalized radio environment monitoring (G-REM) concept to include all aspects of the radio environment. We outline a general, self contained framework which enables radio environment monitoring in standalone devices. The presented G-REM framework comprises of different information sources, sensing methods, sensing modes, and mapping techniques to enable the realization of more reliable, secure, efficient, and faster future wireless networks. The accompanying challenges and future research direction are also provided. | ['Huseyin Arslan', 'Haji M. Furqan', 'Muhammad Sohaib J. Solaija', 'Halise Turkmen'] | 2020-08-14 | null | null | null | null | ['intelligent-communication'] | ['time-series'] | [ 4.98509586e-01 -2.36451887e-02 -1.19038790e-01 -1.50946543e-01
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162f1749-aacf-4dcf-9a6b-b4337e0dc531 | self-supervised-learning-for-large-scale | 2008.10312 | null | https://arxiv.org/abs/2008.10312v2 | https://arxiv.org/pdf/2008.10312v2.pdf | Self-Supervised Learning for Large-Scale Unsupervised Image Clustering | Unsupervised learning has always been appealing to machine learning researchers and practitioners, allowing them to avoid an expensive and complicated process of labeling the data. However, unsupervised learning of complex data is challenging, and even the best approaches show much weaker performance than their supervised counterparts. Self-supervised deep learning has become a strong instrument for representation learning in computer vision. However, those methods have not been evaluated in a fully unsupervised setting. In this paper, we propose a simple scheme for unsupervised classification based on self-supervised representations. We evaluate the proposed approach with several recent self-supervised methods showing that it achieves competitive results for ImageNet classification (39% accuracy on ImageNet with 1000 clusters and 46% with overclustering). We suggest adding the unsupervised evaluation to a set of standard benchmarks for self-supervised learning. The code is available at https://github.com/Randl/kmeans_selfsuper | ['Alex M. Bronstein', 'Evgenii Zheltonozhskii', 'Chaim Baskin', 'Avi Mendelson'] | 2020-08-24 | null | null | null | null | ['image-clustering', 'unsupervised-image-classification'] | ['computer-vision', 'computer-vision'] | [ 1.08907379e-01 1.42674893e-01 -4.83344615e-01 -7.55708933e-01
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56846e59-48ca-417b-a992-3a05e0b56932 | comparative-study-on-supervised-learning | 1701.06421 | null | http://arxiv.org/abs/1701.06421v1 | http://arxiv.org/pdf/1701.06421v1.pdf | Comparative study on supervised learning methods for identifying phytoplankton species | Phytoplankton plays an important role in marine ecosystem. It is defined as a
biological factor to assess marine quality. The identification of phytoplankton
species has a high potential for monitoring environmental, climate changes and
for evaluating water quality. However, phytoplankton species identification is
not an easy task owing to their variability and ambiguity due to thousands of
micro and pico-plankton species. Therefore, the aim of this paper is to build a
framework for identifying phytoplankton species and to perform a comparison on
different features types and classifiers. We propose a new features type
extracted from raw signals of phytoplankton species. We then analyze the
performance of various classifiers on the proposed features type as well as two
other features types for finding the robust one. Through experiments, it is
found that Random Forest using the proposed features gives the best
classification results with average accuracy up to 98.24%. | ['André Bigand', 'Thi-Thu-Hong Phan', 'Emilie Poisson Caillault'] | 2017-01-23 | null | null | null | null | ['pico'] | ['natural-language-processing'] | [-6.17671311e-02 -7.26918161e-01 7.76046455e-01 -9.06135961e-02
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e53802e4-b1a6-4cf9-8674-1d5d2761e621 | distributed-optimization-for-quadratic-cost | 2304.00596 | null | https://arxiv.org/abs/2304.00596v1 | https://arxiv.org/pdf/2304.00596v1.pdf | Distributed Optimization for Quadratic Cost Functions over Large-Scale Networks with Quantized Communication and Finite-Time Convergence | We propose two distributed iterative algorithms that can be used to solve, in finite time, the distributed optimization problem over quadratic local cost functions in large-scale networks. The first algorithm exhibits synchronous operation whereas the second one exhibits asynchronous operation. Both algorithms share salient features. Specifically, the algorithms operate exclusively with quantized values, which means that the information stored, processed and exchanged between neighboring nodes is subject to deterministic uniform quantization. The algorithms rely on event-driven updates in order to reduce energy consumption, communication bandwidth, network congestion, and/or processor usage. Finally, once the algorithms converge, nodes distributively terminate their operation. We prove that our algorithms converge in a finite number of iterations to the exact optimal solution depending on the quantization level, and we present applications of our algorithms to (i) optimal task scheduling for data centers, and (ii) global model aggregation for distributed federated learning. We provide simulations of these applications to illustrate the operation, performance, and advantages of the proposed algorithms. Additionally, it is shown that our proposed algorithms compare favorably to algorithms in the current literature. Quantized communication and asynchronous updates increase the required time to completion, but finite-time operation is maintained. | ['Karl H. Johansson', 'Themistoklis Charalambous', 'Christoforos N. Hadjicostis', 'Evangelia Kalyvianaki', 'Andreas Grammenos', 'Apostolos I. Rikos'] | 2023-04-02 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [-5.75382523e-02 -1.02290899e-01 -1.44793198e-01 -2.38532290e-01
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0812321e-3d00-4b5a-b26e-f7b0c4899fbf | sam-gcnn-a-gated-convolutional-neural-network | 1810.03986 | null | http://arxiv.org/abs/1810.03986v2 | http://arxiv.org/pdf/1810.03986v2.pdf | SAM-GCNN: A Gated Convolutional Neural Network with Segment-Level Attention Mechanism for Home Activity Monitoring | In this paper, we propose a method for home activity monitoring. We
demonstrate our model on dataset of Detection and Classification of Acoustic
Scenes and Events (DCASE) 2018 Challenge Task 5. This task aims to classify
multi-channel audios into one of the provided pre-defined classes. All of these
classes are daily activities performed in a home environment. To tackle this
task, we propose a gated convolutional neural network with segment-level
attention mechanism (SAM-GCNN). The proposed framework is a convolutional model
with two auxiliary modules: a gated convolutional neural network and a
segment-level attention mechanism. Furthermore, we adopted model ensemble to
enhance the capability of generalization of our model. We evaluated our work on
the development dataset of DCASE 2018 Task 5 and achieved competitive
performance, with a macro-averaged F-1 score increasing from 83.76% to 89.33%,
compared with the convolutional baseline system. | ['Wei-Qiang Zhang', 'Ke-Xin He', 'Yu-Han Shen'] | 2018-10-03 | null | null | null | null | ['home-activity-monitoring'] | ['miscellaneous'] | [ 3.92025083e-01 -7.73083791e-02 4.51876163e-01 -3.31791312e-01
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b5a58841-4972-40bf-a114-344e7ac195c0 | deep-reinforcement-learning-for-modeling-chit | 1907.02848 | null | https://arxiv.org/abs/1907.02848v2 | https://arxiv.org/pdf/1907.02848v2.pdf | Deep Reinforcement Learning For Modeling Chit-Chat Dialog With Discrete Attributes | Open domain dialog systems face the challenge of being repetitive and producing generic responses. In this paper, we demonstrate that by conditioning the response generation on interpretable discrete dialog attributes and composed attributes, it helps improve the model perplexity and results in diverse and interesting non-redundant responses. We propose to formulate the dialog attribute prediction as a reinforcement learning (RL) problem and use policy gradients methods to optimize utterance generation using long-term rewards. Unlike existing RL approaches which formulate the token prediction as a policy, our method reduces the complexity of the policy optimization by limiting the action space to dialog attributes, thereby making the policy optimization more practical and sample efficient. We demonstrate this with experimental and human evaluations. | ['Chinnadhurai Sankar', 'Sujith Ravi'] | 2019-07-05 | deep-reinforcement-learning-for-modeling-chit-1 | https://aclanthology.org/W19-5901 | https://aclanthology.org/W19-5901.pdf | ws-2019-9 | ['open-domain-dialog'] | ['natural-language-processing'] | [ 1.29269525e-01 7.16464162e-01 -1.84993863e-01 -8.13206017e-01
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5154ec11-6df2-434d-ba08-9134e41348a9 | csi-based-data-driven-localization | 2304.11455 | null | https://arxiv.org/abs/2304.11455v1 | https://arxiv.org/pdf/2304.11455v1.pdf | CSI-Based Data-driven Localization Frameworking using Small-scale Training Datasets in Single-site MIMO Systems | This work presents a date-driven user localization framework for single-site massive Multiple-Input-Multiple-Output (MIMO) systems. The framework is trained on a geo-tagged Channel State Information (CSI) dataset. Unlike the state-of-the-art Convolutional Neural Network (CNN) models, which require large training datasets to perform well, our method is specifically designed to operate with small-scale training datasets. This makes our approach more practical for real-world scenarios, where collecting a large amount of data can be challenging. Our proposed FC-AE-GPR framework combines two components: a Fully-Connected Auto-Encoder (FC-AE) and a Gaussian Process Regression (GPR) model. Our results show that the GPR model outperforms the CNN model when presented with small training datasets. However, the training complexity of GPR models can become an issue when the input sample size is large. To address this, we propose using the FC-AE to reduce the sample size by encoding the CSI before training the GPR model. Although the FC-AE model may require a larger training dataset initially, we demonstrate that the FC-AE is scenario independent. This means that it can be utilized in new and unseen scenarios without prior retraining. Therefore, adapting the FC-AE-GPR model to a new scenario requires only retraining the GPR model with a small training dataset. | ['Nazanin Rahnavard', 'Farzam Hejazi', 'Katarina Vuckovic'] | 2023-04-22 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [ 8.65695104e-02 4.03027013e-02 1.52878329e-01 -2.03909412e-01
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ae9b2088-232c-4774-a8d7-d3d3ffa21ada | 3rd-place-solution-to-meta-ai-video | 2304.11964 | null | https://arxiv.org/abs/2304.11964v2 | https://arxiv.org/pdf/2304.11964v2.pdf | 3rd Place Solution to Meta AI Video Similarity Challenge | This paper presents our 3rd place solution in both Descriptor Track and Matching Track of the Meta AI Video Similarity Challenge (VSC2022), a competition aimed at detecting video copies. Our approach builds upon existing image copy detection techniques and incorporates several strategies to exploit on the properties of video data, resulting in a simple yet powerful solution. By employing our proposed method, we achieved substantial improvements in accuracy compared to the baseline results (Descriptor Track: 38% improvement, Matching Track: 60% improvement). Our code is publicly available here: https://github.com/line/Meta-AI-Video-Similarity-Challenge-3rd-Place-Solution | ['Rintaro Hasegawa', 'Junki Ishikawa', 'Peifei Zhu', 'Shuhei Yokoo'] | 2023-04-24 | null | null | null | null | ['video-similarity'] | ['computer-vision'] | [ 2.14933261e-01 -6.23832643e-01 -2.38669261e-01 1.13403179e-01
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16a0b633-547f-42c6-bc0e-8ef32712b747 | idql-implicit-q-learning-as-an-actor-critic | 2304.10573 | null | https://arxiv.org/abs/2304.10573v2 | https://arxiv.org/pdf/2304.10573v2.pdf | IDQL: Implicit Q-Learning as an Actor-Critic Method with Diffusion Policies | Effective offline RL methods require properly handling out-of-distribution actions. Implicit Q-learning (IQL) addresses this by training a Q-function using only dataset actions through a modified Bellman backup. However, it is unclear which policy actually attains the values represented by this implicitly trained Q-function. In this paper, we reinterpret IQL as an actor-critic method by generalizing the critic objective and connecting it to a behavior-regularized implicit actor. This generalization shows how the induced actor balances reward maximization and divergence from the behavior policy, with the specific loss choice determining the nature of this tradeoff. Notably, this actor can exhibit complex and multimodal characteristics, suggesting issues with the conditional Gaussian actor fit with advantage weighted regression (AWR) used in prior methods. Instead, we propose using samples from a diffusion parameterized behavior policy and weights computed from the critic to then importance sampled our intended policy. We introduce Implicit Diffusion Q-learning (IDQL), combining our general IQL critic with the policy extraction method. IDQL maintains the ease of implementation of IQL while outperforming prior offline RL methods and demonstrating robustness to hyperparameters. Code is available at https://github.com/philippe-eecs/IDQL. | ['Sergey Levine', 'Jakub Grudzien Kuba', 'Michael Janner', 'Ilya Kostrikov', 'Philippe Hansen-Estruch'] | 2023-04-20 | null | null | null | null | ['q-learning', 'offline-rl'] | ['methodology', 'playing-games'] | [-2.35790938e-01 3.62868607e-01 -7.26976514e-01 -1.37645543e-01
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294ce550-b692-4be8-b34b-7decbfb701a7 | heteroskedastic-geospatial-tracking-with | 2306.02407 | null | https://arxiv.org/abs/2306.02407v1 | https://arxiv.org/pdf/2306.02407v1.pdf | Heteroskedastic Geospatial Tracking with Distributed Camera Networks | Visual object tracking has seen significant progress in recent years. However, the vast majority of this work focuses on tracking objects within the image plane of a single camera and ignores the uncertainty associated with predicted object locations. In this work, we focus on the geospatial object tracking problem using data from a distributed camera network. The goal is to predict an object's track in geospatial coordinates along with uncertainty over the object's location while respecting communication constraints that prohibit centralizing raw image data. We present a novel single-object geospatial tracking data set that includes high-accuracy ground truth object locations and video data from a network of four cameras. We present a modeling framework for addressing this task including a novel backbone model and explore how uncertainty calibration and fine-tuning through a differentiable tracker affect performance. | ['Benjamin M. Marlin', 'Mani Srivastava', 'Deepak Ganesan', 'Ziqi Wang', 'Shiwei Fang', 'Colin Samplawski'] | 2023-06-04 | null | null | null | null | ['object-tracking', 'visual-object-tracking'] | ['computer-vision', 'computer-vision'] | [-1.78722978e-01 -3.04750204e-01 -2.59867579e-01 -3.31421047e-01
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adb49536-557b-4ee6-a74c-9d5fa544afc8 | towards-a-modeling-optimization-and | 2302.02177 | null | https://arxiv.org/abs/2302.02177v1 | https://arxiv.org/pdf/2302.02177v1.pdf | Towards a modeling, optimization and predictive control framework for fed-batch metabolic cybergenetics | Biotechnology offers many opportunities for the sustainable manufacturing of valuable products. The toolbox to optimize bioprocesses includes \textit{extracellular} process elements such as the bioreactor design and mode of operation, medium formulation, culture conditions, feeding rates, etc. However, these elements are frequently insufficient for achieving optimal process performance or precise product composition. One can use metabolic and genetic engineering methods for optimization at the intracellular level. Nevertheless, those are often of static nature, failing when applied to dynamic processes or if disturbances occur. Furthermore, many bioprocesses are optimized empirically and implemented with little-to-no feedback control to counteract disturbances. The concept of cybergenetics has opened new possibilities to optimize bioprocesses by enabling online modulation of the gene expression of metabolism-relevant proteins via external inputs (e.g., light intensity in optogenetics). Here, we fuse cybergenetics with model-based optimization and predictive control for optimizing dynamic bioprocesses. To do so, we propose to use dynamic constraint-based models that integrate the dynamics of metabolic reactions, resource allocation, and inducible gene expression. We formulate a model-based optimal control problem to find the optimal process inputs. Furthermore, we propose using model predictive control to address uncertainties via online feedback. We focus on fed-batch processes, where the substrate feeding rate is an additional optimization variable. As a simulation example, we show the optogenetic control of the ATPase enzyme complex for dynamic modulation of enforced ATP wasting to adjust product yield and productivity. | ['Rolf Findeisen', 'Steffen Klamt', 'Katja Bettenbrock', 'Johannes Pohlodek', 'Bruno Morabito', 'Sebastián Espinel-Ríos'] | 2023-02-04 | null | null | null | null | ['culture'] | ['speech'] | [ 3.64711463e-01 -1.06506817e-01 9.93834343e-03 3.17750454e-01
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9a7eeeec-17de-4e0c-8bdd-6e4064be94f0 | accurate-image-restoration-with-attention | 2210.01427 | null | https://arxiv.org/abs/2210.01427v4 | https://arxiv.org/pdf/2210.01427v4.pdf | Accurate Image Restoration with Attention Retractable Transformer | Recently, Transformer-based image restoration networks have achieved promising improvements over convolutional neural networks due to parameter-independent global interactions. To lower computational cost, existing works generally limit self-attention computation within non-overlapping windows. However, each group of tokens are always from a dense area of the image. This is considered as a dense attention strategy since the interactions of tokens are restrained in dense regions. Obviously, this strategy could result in restricted receptive fields. To address this issue, we propose Attention Retractable Transformer (ART) for image restoration, which presents both dense and sparse attention modules in the network. The sparse attention module allows tokens from sparse areas to interact and thus provides a wider receptive field. Furthermore, the alternating application of dense and sparse attention modules greatly enhances representation ability of Transformer while providing retractable attention on the input image.We conduct extensive experiments on image super-resolution, denoising, and JPEG compression artifact reduction tasks. Experimental results validate that our proposed ART outperforms state-of-the-art methods on various benchmark datasets both quantitatively and visually. We also provide code and models at https://github.com/gladzhang/ART. | ['Xin Yuan', 'Linghe Kong', 'Yongbing Zhang', 'Jinjin Gu', 'Yulun Zhang', 'Jiale Zhang'] | 2022-10-04 | null | null | null | null | ['jpeg-compression-artifact-reduction'] | ['computer-vision'] | [ 1.92228213e-01 -1.93957806e-01 1.87724251e-02 -3.65007035e-02
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ab7a1d80-7446-45b9-99ce-22b8c5d6e908 | gp-net-grasp-proposal-for-mobile-manipulators | 2209.10404 | null | https://arxiv.org/abs/2209.10404v2 | https://arxiv.org/pdf/2209.10404v2.pdf | GP-net: Flexible Viewpoint Grasp Proposal | We present the Grasp Proposal Network (GP-net), a Convolutional Neural Network model which can generate 6-DOF grasps from flexible viewpoints, e.g. as experienced by mobile manipulators. To train GP-net, we synthetically generate a dataset containing depth-images and ground-truth grasp information. In real-world experiments we use the EGAD! grasping benchmark to evaluate GP-net against two commonly used algorithms, the Volumetric Grasping Network (VGN) and the Grasp Pose Detection package (GPD), on a PAL TIAGo mobile manipulator. In contrast to the state-of-the-art methods in robotic grasping, GP-net can be used for grasping objects from flexible, unknown viewpoints without the need to define the workspace and achieves a grasp success of 51.8% compared to 51.1% for VGN and 33.6% for GPD. We provide a ROS package along with our code and pre-trained models at https://aucoroboticsmu.github.io/GP-net/. | ['Rudi Villing', 'John McDonald', 'Anna Konrad'] | 2022-09-21 | null | null | null | null | ['robotic-grasping'] | ['robots'] | [-3.00677240e-01 2.13103756e-01 2.47784913e-01 -1.90793261e-01
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6b699c29-f26e-4c41-9c23-98361dfee152 | geometric-estimation-via-robust-subspace | null | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/4101_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123670460.pdf | Geometric Estimation via Robust Subspace Recovery | Geometric estimation from image point correspondences is the core procedure of many 3D vision problems, which is prevalently accomplished by random sampling techniques. In this paper, we consider the problem from an optimization perspective, to exploit the intrinsic linear structure of point correspondences to assist estimation. We generalize the conventional method to a robust one and extend the previous analysis for linear structure to develop several new algorithms. The proposed solutions essentially address the estimation problem by solving a subspace recovery problem to identify the inliers. Experiments on real-world image datasets for both fundamental matrix and homography estimation demonstrate the superiority of our method over the state-of-the-art in terms of both robustness and accuracy. | ['Junjun Jiang', 'Jiayi Ma', 'Yang Wang', 'Aoxiang Fan', 'Xingyu Jiang'] | null | null | null | null | eccv-2020-8 | ['homography-estimation'] | ['computer-vision'] | [ 1.76661715e-01 -3.31169814e-01 -1.45760402e-01 -1.50278313e-02
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ee40a985-41fe-4ce4-91dd-eb72212e5057 | neural-driven-multi-criteria-tree-search-for | null | null | https://openreview.net/forum?id=vjea2ynL7MW | https://openreview.net/pdf?id=vjea2ynL7MW | Neural-Driven Multi-criteria Tree Search for Paraphrase Generation | A good paraphrase is semantically similar to the original sentence but it must be also well formed, and syntactically different to ensure diversity. To deal with this trade-off, we propose to cast the paraphrase generation task as a multi-objectives search problem on the lattice of text transformations. We use BERT and GPT2 to measure respectively the semantic distance and the correctness of the candidates. We study two search algorithms: Monte-Carlo Tree Search For Paraphrase Generation (MCPG) and Pareto Tree Search (PTS) that we use to explore the huge sets of candidates generated by applying the PPDB-2.0 edition rules. We evaluate this approach on 5 datasets and show that it performs reasonably well and that it outperforms a state-of-the-art edition-based text generation method. | ['Damien Lolive', 'Jonathan Chevelu', 'Tanguy Urvoy', 'Betty Fabre'] | 2020-10-17 | null | null | null | neurips-workshop-lmca-2020-12 | ['paraphrase-generation', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing'] | [ 3.34451973e-01 2.47322679e-01 -1.06819473e-01 -2.46943250e-01
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54c5c792-6b28-4d41-a92a-06f88c5ef0ab | sit-back-and-relax-learning-to-drive | 2305.18953 | null | https://arxiv.org/abs/2305.18953v1 | https://arxiv.org/pdf/2305.18953v1.pdf | Sit Back and Relax: Learning to Drive Incrementally in All Weather Conditions | In autonomous driving scenarios, current object detection models show strong performance when tested in clear weather. However, their performance deteriorates significantly when tested in degrading weather conditions. In addition, even when adapted to perform robustly in a sequence of different weather conditions, they are often unable to perform well in all of them and suffer from catastrophic forgetting. To efficiently mitigate forgetting, we propose Domain-Incremental Learning through Activation Matching (DILAM), which employs unsupervised feature alignment to adapt only the affine parameters of a clear weather pre-trained network to different weather conditions. We propose to store these affine parameters as a memory bank for each weather condition and plug-in their weather-specific parameters during driving (i.e. test time) when the respective weather conditions are encountered. Our memory bank is extremely lightweight, since affine parameters account for less than 2% of a typical object detector. Furthermore, contrary to previous domain-incremental learning approaches, we do not require the weather label when testing and propose to automatically infer the weather condition by a majority voting linear classifier. | ['Horst Bischof', 'Horst Possegger', 'Mateusz Kozinski', 'Marc Masana', 'Jakub Micorek', 'Wei Lin', 'M. Jehanzeb Mirza', 'Stefan Leitner'] | 2023-05-30 | null | null | null | null | ['incremental-learning'] | ['methodology'] | [ 1.14289485e-01 -1.93398476e-01 1.40529916e-01 -6.66338980e-01
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97fa2a9b-26d7-472f-8121-6bc989b23cf5 | graph-neural-networks-for-the-prediction-of-1 | 2208.04852 | null | https://arxiv.org/abs/2208.04852v1 | https://arxiv.org/pdf/2208.04852v1.pdf | Graph neural networks for the prediction of molecular structure-property relationships | Molecular property prediction is of crucial importance in many disciplines such as drug discovery, molecular biology, or material and process design. The frequently employed quantitative structure-property/activity relationships (QSPRs/QSARs) characterize molecules by descriptors which are then mapped to the properties of interest via a linear or nonlinear model. In contrast, graph neural networks, a novel machine learning method, directly work on the molecular graph, i.e., a graph representation where atoms correspond to nodes and bonds correspond to edges. GNNs allow to learn properties in an end-to-end fashion, thereby avoiding the need for informative descriptors as in QSPRs/QSARs. GNNs have been shown to achieve state-of-the-art prediction performance on various property predictions tasks and represent an active field of research. We describe the fundamentals of GNNs and demonstrate the application of GNNs via two examples for molecular property prediction. | ['Artur M. Schweidtmann', 'Alexander Mitsos', 'Manuel Dahmen', 'Qinghe Gao', 'Jan G. Rittig'] | 2022-07-25 | null | null | null | null | ['molecular-property-prediction'] | ['miscellaneous'] | [ 6.17972851e-01 1.57681614e-01 -8.43382955e-01 -1.52178347e-01
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6f8b15b0-6d38-4e6e-97ac-52dc92e5e513 | satvsr-scenario-adaptive-transformer-for | 2211.08703 | null | https://arxiv.org/abs/2211.08703v1 | https://arxiv.org/pdf/2211.08703v1.pdf | SATVSR: Scenario Adaptive Transformer for Cross Scenarios Video Super-Resolution | Video Super-Resolution (VSR) aims to recover sequences of high-resolution (HR) frames from low-resolution (LR) frames. Previous methods mainly utilize temporally adjacent frames to assist the reconstruction of target frames. However, in the real world, there is a lot of irrelevant information in adjacent frames of videos with fast scene switching, these VSR methods cannot adaptively distinguish and select useful information. In contrast, with a transformer structure suitable for temporal tasks, we devise a novel adaptive scenario video super-resolution method. Specifically, we use optical flow to label the patches in each video frame, only calculate the attention of patches with the same label. Then select the most relevant label among them to supplement the spatial-temporal information of the target frame. This design can directly make the supplementary information come from the same scene as much as possible. We further propose a cross-scale feature aggregation module to better handle the scale variation problem. Compared with other video super-resolution methods, our method not only achieves significant performance gains on single-scene videos but also has better robustness on cross-scene datasets. | ['Tieru Wu', 'Yongjie Chen'] | 2022-11-16 | null | null | null | null | ['video-super-resolution'] | ['computer-vision'] | [ 3.50202143e-01 -5.27938128e-01 -2.49779761e-01 -1.62187099e-01
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d92d1f42-0fd4-484b-9ff2-9a5afc029ece | robust-tensor-decomposition-for-image | 2005.04605 | null | https://arxiv.org/abs/2005.04605v1 | https://arxiv.org/pdf/2005.04605v1.pdf | Robust Tensor Decomposition for Image Representation Based on Generalized Correntropy | Traditional tensor decomposition methods, e.g., two dimensional principal component analysis and two dimensional singular value decomposition, that minimize mean square errors, are sensitive to outliers. To overcome this problem, in this paper we propose a new robust tensor decomposition method using generalized correntropy criterion (Corr-Tensor). A Lagrange multiplier method is used to effectively optimize the generalized correntropy objective function in an iterative manner. The Corr-Tensor can effectively improve the robustness of tensor decomposition with the existence of outliers without introducing any extra computational cost. Experimental results demonstrated that the proposed method significantly reduces the reconstruction error on face reconstruction and improves the accuracies on handwritten digit recognition and facial image clustering. | ['Yongsheng Gao', 'Miaohua Zhang', 'Michael Blumenstein', 'Changming Sun'] | 2020-05-10 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [-4.28937465e-01 -6.78168058e-01 2.75429696e-01 -1.13785811e-01
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2b2bf0b0-5508-4c19-81c4-0b574ee8b5e9 | swarm-intelligence-algorithms-applied-to | 2111.02212 | null | https://arxiv.org/abs/2111.02212v5 | https://arxiv.org/pdf/2111.02212v5.pdf | A swarm intelligence-based robust solution for Virtual Reference Feedback Tuning | This work proposes the inclusion of an $\mathcal{H}_{\infty}$ robustness constraint to the Virtual Reference Feedback Tuning (VRFT) cost function, which is solved by metaheuristic optimization with only a single batch of data (one-shot). The $\mathcal{H}_{\infty}$ norm of the sensitivity transfer function is estimated in a data-driven fashion, based on the regularized estimation of the system's impulse response. Four different swarm intelligence algorithms are chosen to be evaluated and compared at the optimization problem. Two real-world inspired examples are used to illustrate the proposed method through a Monte Carlo experiment with 50 runs. To compare the swarm intelligence algorithms to each other, 50 search agents have been adopted, with a maximum number of iterations of 100. | ['Y. R. de Novaes', 'C. L. Remes', 'L. V. Fiorio'] | 2021-11-03 | null | null | null | null | ['metaheuristic-optimization'] | ['methodology'] | [ 2.69213110e-01 1.15777798e-01 2.65596062e-01 2.31685806e-02
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cbd75eb5-a908-4e77-b148-5c641177423e | vector-space-models-for-scientific-document | null | null | https://aclanthology.org/W15-1525 | https://aclanthology.org/W15-1525.pdf | Vector Space Models for Scientific Document Summarization | null | ['Sashka Davis', 'John Conroy'] | 2015-06-01 | null | null | null | ws-2015-6 | ['scientific-article-summarization'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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1e9b1c2a-eadf-4fd4-a184-893bf8eb42bc | deep-lighting-environment-map-estimation-from | 2005.08000 | null | https://arxiv.org/abs/2005.08000v1 | https://arxiv.org/pdf/2005.08000v1.pdf | Deep Lighting Environment Map Estimation from Spherical Panoramas | Estimating a scene's lighting is a very important task when compositing synthetic content within real environments, with applications in mixed reality and post-production. In this work we present a data-driven model that estimates an HDR lighting environment map from a single LDR monocular spherical panorama. In addition to being a challenging and ill-posed problem, the lighting estimation task also suffers from a lack of facile illumination ground truth data, a fact that hinders the applicability of data-driven methods. We approach this problem differently, exploiting the availability of surface geometry to employ image-based relighting as a data generator and supervision mechanism. This relies on a global Lambertian assumption that helps us overcome issues related to pre-baked lighting. We relight our training data and complement the model's supervision with a photometric loss, enabled by a differentiable image-based relighting technique. Finally, since we predict spherical spectral coefficients, we show that by imposing a distribution prior on the predicted coefficients, we can greatly boost performance. Code and models available at https://vcl3d.github.io/DeepPanoramaLighting. | ['Petros Daras', 'Federico Alvarez', 'Dimitrios Zarpalas', 'Vasileios Gkitsas', 'Nikolaos Zioulis'] | 2020-05-16 | null | null | null | null | ['lighting-estimation'] | ['computer-vision'] | [ 4.14677531e-01 2.06124736e-03 3.49460721e-01 -3.98013055e-01
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b5097d90-feac-46e3-ba53-9e6ade5a9b3a | pulsenet-deep-learning-ecg-signal | 2305.15424 | null | https://arxiv.org/abs/2305.15424v2 | https://arxiv.org/pdf/2305.15424v2.pdf | PulseNet: Deep Learning ECG-signal classification using random augmentation policy and continous wavelet transform for canines | Evaluating canine electrocardiograms (ECG) require skilled veterinarians, but current availability of veterinary cardiologists for ECG interpretation and diagnostic support is limited. Developing tools for automated assessment of ECG sequences can improve veterinary care by providing clinicians real-time results and decision support tools. We implement a deep convolutional neural network (CNN) approach for classifying canine electrocardiogram sequences as either normal or abnormal. ECG records are converted into 8 second Lead II sequences and classified as either normal (no evidence of cardiac abnormalities) or abnormal (presence of one or more cardiac abnormalities). For training ECG sequences are randomly augmented using RandomAugmentECG, a new augmentation library implemented specifically for this project. Each chunk is then is converted using a continuous wavelet transform into a 2D scalogram. The 2D scalogram are then classified as either normal or abnormal by a binary CNN classifier. Experimental results are validated against three boarded veterinary cardiologists achieving an AUC-ROC score of 0.9506 on test dataset matching human level performance. Additionally, we describe model deployment to Microsoft Azure using an MLOps approach. To our knowledge, this work is one of the first attempts to implement a deep learning model to automatically classify ECG sequences for canines.Implementing automated ECG classification will enhance veterinary care through improved diagnostic performance and increased clinic efficiency. | ['Mark Parkinson', 'Xiaoli Qiao', 'Emil Walleser', 'Norbert Sithirangathan', 'Michael Fitzke', 'Fernando Junior', 'Oliver Roman Stiel', 'Jennifer Schneiderman', 'Federica Marchesotti', 'Roberto Santilli', 'Andre Dourson'] | 2023-05-17 | null | null | null | null | ['ecg-classification'] | ['medical'] | [ 4.33692098e-01 -1.04760081e-01 4.60981131e-02 -6.65680468e-01
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7970aa88-96e7-402c-8485-0392f2e9ecfd | gaussian-smoothed-imbalance-data-improves | 2302.08650 | null | https://arxiv.org/abs/2302.08650v1 | https://arxiv.org/pdf/2302.08650v1.pdf | Gaussian-smoothed Imbalance Data Improves Speech Emotion Recognition | In speech emotion recognition tasks, models learn emotional representations from datasets. We find the data distribution in the IEMOCAP dataset is very imbalanced, which may harm models to learn a better representation. To address this issue, we propose a novel Pairwise-emotion Data Distribution Smoothing (PDDS) method. PDDS considers that the distribution of emotional data should be smooth in reality, then applies Gaussian smoothing to emotion-pairs for constructing a new training set with a smoother distribution. The required new data are complemented using the mixup augmentation. As PDDS is model and modality agnostic, it is evaluated with three SOTA models on the IEMOCAP dataset. The experimental results show that these models are improved by 0.2\% - 4.8\% and 1.5\% - 5.9\% in terms of WA and UA. In addition, an ablation study demonstrates that the key advantage of PDDS is the reasonable data distribution rather than a simple data augmentation. | ['Ying Zhou', 'Wenxin Xu', 'Hexin Jiang', 'Xuefeng Liang'] | 2023-02-17 | null | null | null | null | ['speech-emotion-recognition'] | ['speech'] | [-1.36114076e-01 3.64972174e-01 -1.43039525e-01 -6.32903576e-01
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f141f86f-2824-4a3a-8de6-1bcfc2de512f | stock-price-prediction-using-temporal-graph | 2303.09406 | null | https://arxiv.org/abs/2303.09406v1 | https://arxiv.org/pdf/2303.09406v1.pdf | Stock Price Prediction Using Temporal Graph Model with Value Chain Data | Stock price prediction is a crucial element in financial trading as it allows traders to make informed decisions about buying, selling, and holding stocks. Accurate predictions of future stock prices can help traders optimize their trading strategies and maximize their profits. In this paper, we introduce a neural network-based stock return prediction method, the Long Short-Term Memory Graph Convolutional Neural Network (LSTM-GCN) model, which combines the Graph Convolutional Network (GCN) and Long Short-Term Memory (LSTM) Cells. Specifically, the GCN is used to capture complex topological structures and spatial dependence from value chain data, while the LSTM captures temporal dependence and dynamic changes in stock returns data. We evaluated the LSTM-GCN model on two datasets consisting of constituents of Eurostoxx 600 and S&P 500. Our experiments demonstrate that the LSTM-GCN model can capture additional information from value chain data that are not fully reflected in price data, and the predictions outperform baseline models on both datasets. | ['Sandra Paterlini', 'Chang Liu'] | 2023-03-07 | null | null | null | null | ['stock-price-prediction'] | ['time-series'] | [-6.48626149e-01 -1.75674930e-01 -3.64844292e-01 -1.85838997e-01
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c07db852-4be0-4b22-b6f9-eb31ec6881d2 | multi-modal-text-recognition-networks | 2111.15263 | null | https://arxiv.org/abs/2111.15263v3 | https://arxiv.org/pdf/2111.15263v3.pdf | Multi-modal Text Recognition Networks: Interactive Enhancements between Visual and Semantic Features | Linguistic knowledge has brought great benefits to scene text recognition by providing semantics to refine character sequences. However, since linguistic knowledge has been applied individually on the output sequence, previous methods have not fully utilized the semantics to understand visual clues for text recognition. This paper introduces a novel method, called Multi-modAl Text Recognition Network (MATRN), that enables interactions between visual and semantic features for better recognition performances. Specifically, MATRN identifies visual and semantic feature pairs and encodes spatial information into semantic features. Based on the spatial encoding, visual and semantic features are enhanced by referring to related features in the other modality. Furthermore, MATRN stimulates combining semantic features into visual features by hiding visual clues related to the character in the training phase. Our experiments demonstrate that MATRN achieves state-of-the-art performances on seven benchmarks with large margins, while naive combinations of two modalities show less-effective improvements. Further ablative studies prove the effectiveness of our proposed components. Our implementation is available at https://github.com/wp03052/MATRN. | ['Sungrae Park', 'Yoonsik Kim', 'Byeonghu Na'] | 2021-11-30 | null | null | null | null | ['scene-text-recognition'] | ['computer-vision'] | [ 2.66488582e-01 -4.76165563e-01 -2.56966680e-01 -3.22797954e-01
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f10e5ccb-55b0-4d3a-a311-a6d75a9cd9fa | resonant-machine-learning-based-on-complex | 1908.05377 | null | https://arxiv.org/abs/1908.05377v3 | https://arxiv.org/pdf/1908.05377v3.pdf | Resonant Machine Learning Based on Complex Growth Transform Dynamical Systems | Traditional energy-based learning models associate a single energy metric to each configuration of variables involved in the underlying optimization process. Such models associate the lowest energy state to the optimal configuration of variables under consideration, and are thus inherently dissipative. In this paper we propose an energy-efficient learning framework that exploits structural and functional similarities between a machine learning network and a general electrical network satisfying the Tellegen's theorem. In contrast to the standard energy-based models, the proposed formulation associates two energy components, namely, active and reactive energy to the network. This ensures that the network's active-power is dissipated only during the process of learning, whereas the reactive-power is maintained to be zero at all times. As a result, in steady-state, the learned parameters are stored and self-sustained by electrical resonance determined by the network's nodal inductances and capacitances. Based on this approach, this paper introduces three novel concepts: (a) A learning framework where the network's active-power dissipation is used as a regularization for a learning objective function that is subjected to zero total reactive-power constraint; (b) A dynamical system based on complex-domain, continuous-time growth transforms which optimizes the learning objective function and drives the network towards electrical resonance under steady-state operation; and (c) An annealing procedure that controls the trade-off between active-power dissipation and the speed of convergence. As a representative example, we show how the proposed framework can be used for designing resonant support vector machines (SVMs), where we show that the support-vectors correspond to an LC network with self-sustained oscillations. | ['Shantanu Chakrabartty', 'Oindrila Chatterjee'] | 2019-08-15 | null | null | null | null | ['novel-concepts'] | ['reasoning'] | [ 2.46623218e-01 3.09442729e-01 -4.90455776e-01 1.24530837e-01
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270140db-981f-42f1-8f5c-47a714ba6dda | generative-image-inpainting-with-segmentation | 2303.13133 | null | https://arxiv.org/abs/2303.13133v1 | https://arxiv.org/pdf/2303.13133v1.pdf | Generative Image Inpainting with Segmentation Confusion Adversarial Training and Contrastive Learning | This paper presents a new adversarial training framework for image inpainting with segmentation confusion adversarial training (SCAT) and contrastive learning. SCAT plays an adversarial game between an inpainting generator and a segmentation network, which provides pixel-level local training signals and can adapt to images with free-form holes. By combining SCAT with standard global adversarial training, the new adversarial training framework exhibits the following three advantages simultaneously: (1) the global consistency of the repaired image, (2) the local fine texture details of the repaired image, and (3) the flexibility of handling images with free-form holes. Moreover, we propose the textural and semantic contrastive learning losses to stabilize and improve our inpainting model's training by exploiting the feature representation space of the discriminator, in which the inpainting images are pulled closer to the ground truth images but pushed farther from the corrupted images. The proposed contrastive losses better guide the repaired images to move from the corrupted image data points to the real image data points in the feature representation space, resulting in more realistic completed images. We conduct extensive experiments on two benchmark datasets, demonstrating our model's effectiveness and superiority both qualitatively and quantitatively. | ['Dongming Lu', 'Wei Xing', 'Jiafu Chen', 'Zhanjie Zhang', 'Zhizhong Wang', 'Ailin Li', 'Lei Zhao', 'Zhiwen Zuo'] | 2023-03-23 | null | null | null | null | ['image-inpainting'] | ['computer-vision'] | [ 3.34651351e-01 2.50162929e-01 -8.88285264e-02 3.42040136e-02
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55ecdf3e-17c0-490b-924d-bf7bff9dc023 | point-cloud-color-constancy | 2111.11280 | null | https://arxiv.org/abs/2111.11280v1 | https://arxiv.org/pdf/2111.11280v1.pdf | Point Cloud Color Constancy | In this paper, we present Point Cloud Color Constancy, in short PCCC, an illumination chromaticity estimation algorithm exploiting a point cloud. We leverage the depth information captured by the time-of-flight (ToF) sensor mounted rigidly with the RGB sensor, and form a 6D cloud where each point contains the coordinates and RGB intensities, noted as (x,y,z,r,g,b). PCCC applies the PointNet architecture to the color constancy problem, deriving the illumination vector point-wise and then making a global decision about the global illumination chromaticity. On two popular RGB-D datasets, which we extend with illumination information, as well as on a novel benchmark, PCCC obtains lower error than the state-of-the-art algorithms. Our method is simple and fast, requiring merely 16*16-size input and reaching speed over 500 fps, including the cost of building the point cloud and net inference. | ['Jiri Matas', 'Yuhan Dong', 'Sibo Feng', 'Yanlin Qian', 'Xiaoyan Xing'] | 2021-11-22 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Xing_Point_Cloud_Color_Constancy_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Xing_Point_Cloud_Color_Constancy_CVPR_2022_paper.pdf | cvpr-2022-1 | ['color-constancy'] | ['computer-vision'] | [ 2.73606535e-02 -3.72793913e-01 2.53074855e-01 -2.41086423e-01
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cd48f16d-856e-4ec0-bcc4-2c7c5252abca | convergence-bounds-for-nonlinear-least-1 | 2208.10954 | null | https://arxiv.org/abs/2208.10954v2 | https://arxiv.org/pdf/2208.10954v2.pdf | Convergence bounds for local least squares approximation | We consider the problem of approximating a function in a general nonlinear subset of $L^2$, when only a weighted Monte Carlo estimate of the $L^2$-norm can be computed. Of particular interest in this setting is the concept of sample complexity, the number of sample points that are necessary to achieve a prescribed error with high probability. Reasonable worst-case bounds for this quantity exist only for particular model classes, like linear spaces or sets of sparse vectors. For more general sets, like tensor networks or neural networks, the currently existing bounds are very pessimistic. By restricting the model class to a neighbourhood of the best approximation, we can derive improved worst-case bounds for the sample complexity. When the considered neighbourhood is a manifold with positive local reach, its sample complexity can be estimated by means of the sample complexities of the tangent and normal spaces and the manifold's curvature. | ['Philipp Trunschke'] | 2022-08-23 | null | null | null | null | ['tensor-networks'] | ['methodology'] | [-1.76522657e-01 4.69445795e-01 -5.34275733e-03 -1.37604043e-01
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bd8552da-5972-49bc-8b30-80ba70cbc5a0 | on-the-interaction-of-regularization-factors | null | null | https://aclanthology.org/2022.eamt-1.14 | https://aclanthology.org/2022.eamt-1.14.pdf | On the Interaction of Regularization Factors in Low-resource Neural Machine Translation | We explore the roles and interactions of the hyper-parameters governing regularization, and propose a range of values applicable to low-resource neural machine translation. We demonstrate that default or recommended values for high-resource settings are not optimal for low-resource ones, and that more aggressive regularization is needed when resources are scarce, in proportion to their scarcity. We explain our observations by the generalization abilities of sharp vs. flat basins in the loss landscape of a neural network. Results for four regularization factors corroborate our claim: batch size, learning rate, dropout rate, and gradient clipping. Moreover, we show that optimal results are obtained when using several of these factors, and that our findings generalize across datasets of different sizes and languages. | ['Andrei Popescu-Belis', 'None Àlex R. Atrio'] | null | null | null | null | eamt-2022-6 | ['low-resource-neural-machine-translation'] | ['natural-language-processing'] | [ 1.26893073e-01 -3.44179757e-03 -6.16804659e-01 -2.69979179e-01
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1e68af2d-cae4-42ba-b504-03dae2a9ddd6 | colde-a-depth-estimation-framework-for | 2111.10371 | null | https://arxiv.org/abs/2111.10371v1 | https://arxiv.org/pdf/2111.10371v1.pdf | ColDE: A Depth Estimation Framework for Colonoscopy Reconstruction | One of the key elements of reconstructing a 3D mesh from a monocular video is generating every frame's depth map. However, in the application of colonoscopy video reconstruction, producing good-quality depth estimation is challenging. Neural networks can be easily fooled by photometric distractions or fail to capture the complex shape of the colon surface, predicting defective shapes that result in broken meshes. Aiming to fundamentally improve the depth estimation quality for colonoscopy 3D reconstruction, in this work we have designed a set of training losses to deal with the special challenges of colonoscopy data. For better training, a set of geometric consistency objectives was developed, using both depth and surface normal information. Also, the classic photometric loss was extended with feature matching to compensate for illumination noise. With the training losses powerful enough, our self-supervised framework named ColDE is able to produce better depth maps of colonoscopy data as compared to the previous work utilizing prior depth knowledge. Used in reconstruction, our network is able to reconstruct good-quality colon meshes in real-time without any post-processing, making it the first to be clinically applicable. | ['Stephen M. Pizer', 'Shuxian Wang', 'Julian G. Rosenman', 'Sarah K. McGill', 'Samuel Ehrenstein', 'Jan-Michael Frahm', 'Yubo Zhang'] | 2021-11-19 | null | null | null | null | ['video-reconstruction'] | ['computer-vision'] | [ 3.03800315e-01 3.83351177e-01 1.19739324e-01 -2.66002208e-01
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412a1a52-8bde-4de3-b6a1-c4712fd35c82 | quantum-neural-network-compression | 2207.01578 | null | https://arxiv.org/abs/2207.01578v2 | https://arxiv.org/pdf/2207.01578v2.pdf | Quantum Neural Network Compression | Model compression, such as pruning and quantization, has been widely applied to optimize neural networks on resource-limited classical devices. Recently, there are growing interest in variational quantum circuits (VQC), that is, a type of neural network on quantum computers (a.k.a., quantum neural networks). It is well known that the near-term quantum devices have high noise and limited resources (i.e., quantum bits, qubits); yet, how to compress quantum neural networks has not been thoroughly studied. One might think it is straightforward to apply the classical compression techniques to quantum scenarios. However, this paper reveals that there exist differences between the compression of quantum and classical neural networks. Based on our observations, we claim that the compilation/traspilation has to be involved in the compression process. On top of this, we propose the very first systematical framework, namely CompVQC, to compress quantum neural networks (QNNs).In CompVQC, the key component is a novel compression algorithm, which is based on the alternating direction method of multipliers (ADMM) approach. Experiments demonstrate the advantage of the CompVQC, reducing the circuit depth (almost over 2.5 %) with a negligible accuracy drop (<1%), which outperforms other competitors. Another promising truth is our CompVQC can indeed promote the robustness of the QNN on the near-term noisy quantum devices. | ['Weiwen Jiang', 'Yanzhi Wang', 'Youzuo Lin', 'Zhepeng Wang', 'Peiyan Dong', 'Zhirui Hu'] | 2022-07-04 | null | null | null | null | ['neural-network-compression', 'neural-network-compression'] | ['methodology', 'miscellaneous'] | [ 5.48342764e-01 8.70949328e-02 -2.96607916e-03 -3.85654159e-02
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02bf8ee9-1570-4ddf-a008-a46ee195d1ab | modeling-electronic-health-record-data-using | 2206.01436 | null | https://arxiv.org/abs/2206.01436v1 | https://arxiv.org/pdf/2206.01436v1.pdf | Modeling electronic health record data using a knowledge-graph-embedded topic model | The rapid growth of electronic health record (EHR) datasets opens up promising opportunities to understand human diseases in a systematic way. However, effective extraction of clinical knowledge from the EHR data has been hindered by its sparsity and noisy information. We present KG-ETM, an end-to-end knowledge graph-based multimodal embedded topic model. KG-ETM distills latent disease topics from EHR data by learning the embedding from the medical knowledge graphs. We applied KG-ETM to a large-scale EHR dataset consisting of over 1 million patients. We evaluated its performance based on EHR reconstruction and drug imputation. KG-ETM demonstrated superior performance over the alternative methods on both tasks. Moreover, our model learned clinically meaningful graph-informed embedding of the EHR codes. In additional, our model is also able to discover interpretable and accurate patient representations for patient stratification and drug recommendations. | ['Yue Li', 'David Buckeridge', 'Aman Verma', 'Ahmad Pesaranghader', 'Yuesong Zou'] | 2022-06-03 | null | null | null | null | ['clinical-knowledge'] | ['miscellaneous'] | [ 8.58904719e-02 7.07736850e-01 -4.30043280e-01 -5.74162900e-01
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2f2741cd-1946-498e-8657-1dc42d5d8f26 | differentially-private-community-detection | 2202.00636 | null | https://arxiv.org/abs/2202.00636v1 | https://arxiv.org/pdf/2202.00636v1.pdf | Differentially Private Community Detection for Stochastic Block Models | The goal of community detection over graphs is to recover underlying labels/attributes of users (e.g., political affiliation) given the connectivity between users (represented by adjacency matrix of a graph). There has been significant recent progress on understanding the fundamental limits of community detection when the graph is generated from a stochastic block model (SBM). Specifically, sharp information theoretic limits and efficient algorithms have been obtained for SBMs as a function of $p$ and $q$, which represent the intra-community and inter-community connection probabilities. In this paper, we study the community detection problem while preserving the privacy of the individual connections (edges) between the vertices. Focusing on the notion of $(\epsilon, \delta)$-edge differential privacy (DP), we seek to understand the fundamental tradeoffs between $(p, q)$, DP budget $(\epsilon, \delta)$, and computational efficiency for exact recovery of the community labels. To this end, we present and analyze the associated information-theoretic tradeoffs for three broad classes of differentially private community recovery mechanisms: a) stability based mechanism; b) sampling based mechanisms; and c) graph perturbation mechanisms. Our main findings are that stability and sampling based mechanisms lead to a superior tradeoff between $(p,q)$ and the privacy budget $(\epsilon, \delta)$; however this comes at the expense of higher computational complexity. On the other hand, albeit low complexity, graph perturbation mechanisms require the privacy budget $\epsilon$ to scale as $\Omega(\log(n))$ for exact recovery. To the best of our knowledge, this is the first work to study the impact of privacy constraints on the fundamental limits for community detection. | ['Ravi Tandon', 'Anil Vullikanti', 'Dung Nguyen', 'Mohamed Seif'] | 2022-01-31 | null | null | null | null | ['stochastic-block-model'] | ['graphs'] | [ 3.19503576e-01 2.60275364e-01 -4.98785861e-02 7.47552738e-02
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d7dddd1d-0ed0-4d08-99c8-8c2739cdec0f | hop-union-generate-explainable-multi-hop | 2305.14237 | null | https://arxiv.org/abs/2305.14237v1 | https://arxiv.org/pdf/2305.14237v1.pdf | HOP, UNION, GENERATE: Explainable Multi-hop Reasoning without Rationale Supervision | Explainable multi-hop question answering (QA) not only predicts answers but also identifies rationales, i. e. subsets of input sentences used to derive the answers. This problem has been extensively studied under the supervised setting, where both answer and rationale annotations are given. Because rationale annotations are expensive to collect and not always available, recent efforts have been devoted to developing methods that do not rely on supervision for rationales. However, such methods have limited capacities in modeling interactions between sentences, let alone reasoning across multiple documents. This work proposes a principled, probabilistic approach for training explainable multi-hop QA systems without rationale supervision. Our approach performs multi-hop reasoning by explicitly modeling rationales as sets, enabling the model to capture interactions between documents and sentences within a document. Experimental results show that our approach is more accurate at selecting rationales than the previous methods, while maintaining similar accuracy in predicting answers. | ['Alexander M. Rush', 'Claire Cardie', 'Justin T. Chiu', 'Wenting Zhao'] | 2023-05-23 | null | null | null | null | ['multi-hop-question-answering'] | ['knowledge-base'] | [ 3.39396596e-01 8.17676008e-01 -1.69372812e-01 -8.02159965e-01
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68f0f45d-d24e-4c64-ad56-dcf0ab6e482c | evaluating-deception-detection-model | 2104.11729 | null | https://arxiv.org/abs/2104.11729v1 | https://arxiv.org/pdf/2104.11729v1.pdf | Evaluating Deception Detection Model Robustness To Linguistic Variation | With the increasing use of machine-learning driven algorithmic judgements, it is critical to develop models that are robust to evolving or manipulated inputs. We propose an extensive analysis of model robustness against linguistic variation in the setting of deceptive news detection, an important task in the context of misinformation spread online. We consider two prediction tasks and compare three state-of-the-art embeddings to highlight consistent trends in model performance, high confidence misclassifications, and high impact failures. By measuring the effectiveness of adversarial defense strategies and evaluating model susceptibility to adversarial attacks using character- and word-perturbed text, we find that character or mixed ensemble models are the most effective defenses and that character perturbation-based attack tactics are more successful. | ['Svitlana Volkova', 'Dustin Arendt', 'Robin Cosbey', 'Ellyn Ayton', 'Maria Glenski'] | 2021-04-23 | null | https://aclanthology.org/2021.socialnlp-1.6 | https://aclanthology.org/2021.socialnlp-1.6.pdf | naacl-socialnlp-2021-6 | ['deception-detection'] | ['miscellaneous'] | [ 8.69641975e-02 -4.02756393e-01 -1.55535713e-01 -9.09665152e-02
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36d6503d-188f-49ae-a7b0-46b885834070 | the-dirha-simulated-corpus | null | null | https://aclanthology.org/L14-1516 | https://aclanthology.org/L14-1516.pdf | The DIRHA simulated corpus | This paper describes a multi-microphone multi-language acoustic corpus being developed under the EC project Distant-speech Interaction for Robust Home Applications (DIRHA). The corpus is composed of several sequences obtained by convolution of dry acoustic events with more than 9000 impulse responses measured in a real apartment equipped with 40 microphones. The acoustic events include in-domain sentences of different typologies uttered by native speakers in four different languages and non-speech events representing typical domestic noises. To increase the realism of the resulting corpus, background noises were recorded in the real home environment and then added to the generated sequences. The purpose of this work is to describe the simulation procedure and the data sets that were created and used to derive the corpus. The corpus contains signals of different characteristics making it suitable for various multi-microphone signal processing and distant speech recognition tasks. | ['ro', 'Maurizio Omologo', 'Mirco Ravanelli', 'Luca Cristoforetti', 'Aless Sosi', 'Alberto Abad', 'Petros Maragos', 'Martin Hagmueller'] | 2014-05-01 | null | null | null | lrec-2014-5 | ['dialogue-management', 'distant-speech-recognition'] | ['natural-language-processing', 'speech'] | [ 6.86688125e-02 -4.01888400e-01 1.01640439e+00 -3.92986864e-01
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7d43093d-e237-4fda-bb51-cd2d703fd594 | unsupervised-speech-domain-adaptation-based | 1904.06086 | null | http://arxiv.org/abs/1904.06086v1 | http://arxiv.org/pdf/1904.06086v1.pdf | Unsupervised Speech Domain Adaptation Based on Disentangled Representation Learning for Robust Speech Recognition | In general, the performance of automatic speech recognition (ASR) systems is
significantly degraded due to the mismatch between training and test
environments. Recently, a deep-learning-based image-to-image translation
technique to translate an image from a source domain to a desired domain was
presented, and cycle-consistent adversarial network (CycleGAN) was applied to
learn a mapping for speech-to-speech conversion from a speaker to a target
speaker. However, this method might not be adequate to remove corrupting noise
components for robust ASR because it was designed to convert speech itself. In
this paper, we propose a domain adaptation method based on generative
adversarial nets (GANs) with disentangled representation learning to achieve
robustness in ASR systems. In particular, two separated encoders, context and
domain encoders, are introduced to learn distinct latent variables. The latent
variables allow us to convert the domain of speech according to its context and
domain representation. We improved word accuracies by 6.55~15.70\% for the
CHiME4 challenge corpus by applying a noisy-to-clean environment adaptation for
robust ASR. In addition, similar to the method based on the CycleGAN, this
method can be used for gender adaptation in gender-mismatched recognition. | ['Hyung-Min Park', 'Jong-Hyeon Park', 'Myungwoo Oh'] | 2019-04-12 | null | null | null | null | ['robust-speech-recognition'] | ['speech'] | [ 6.27069056e-01 1.58268467e-01 1.72106504e-01 -4.86954361e-01
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79973f60-82b9-4501-b73b-1350d423106f | tradeoffs-in-resampling-and-filtering-for | 2209.00127 | null | https://arxiv.org/abs/2209.00127v1 | https://arxiv.org/pdf/2209.00127v1.pdf | Tradeoffs in Resampling and Filtering for Imbalanced Classification | Imbalanced classification problems are extremely common in natural language processing and are solved using a variety of resampling and filtering techniques, which often involve making decisions on how to select training data or decide which test examples should be labeled by the model. We examine the tradeoffs in model performance involved in choices of training sample and filter training and test data in heavily imbalanced token classification task and examine the relationship between the magnitude of these tradeoffs and the base rate of the phenomenon of interest. In experiments on sequence tagging to detect rare phenomena in English and Arabic texts, we find that different methods of selecting training data bring tradeoffs in effectiveness and efficiency. We also see that in highly imbalanced cases, filtering test data using first-pass retrieval models is as important for model performance as selecting training data. The base rate of a rare positive class has a clear effect on the magnitude of the changes in performance caused by the selection of training or test data. As the base rate increases, the differences brought about by those choices decreases. | ['David Smith', 'Ryan Muther'] | 2022-08-31 | null | null | null | null | ['imbalanced-classification'] | ['miscellaneous'] | [ 3.04169655e-01 -2.34558046e-01 -4.86024320e-01 -6.17578983e-01
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97599ab2-f316-405d-8dd2-97a67dd9e395 | structured-sparse-method-for-hyperspectral | 1403.4682 | null | http://arxiv.org/abs/1403.4682v1 | http://arxiv.org/pdf/1403.4682v1.pdf | Structured Sparse Method for Hyperspectral Unmixing | Hyperspectral Unmixing (HU) has received increasing attention in the past
decades due to its ability of unveiling information latent in hyperspectral
data. Unfortunately, most existing methods fail to take advantage of the
spatial information in data. To overcome this limitation, we propose a
Structured Sparse regularized Nonnegative Matrix Factorization (SS-NMF) method
from the following two aspects. First, we incorporate a graph Laplacian to
encode the manifold structures embedded in the hyperspectral data space. In
this way, the highly similar neighboring pixels can be grouped together.
Second, the lasso penalty is employed in SS-NMF for the fact that pixels in the
same manifold structure are sparsely mixed by a common set of relevant bases.
These two factors act as a new structured sparse constraint. With this
constraint, our method can learn a compact space, where highly similar pixels
are grouped to share correlated sparse representations. Experiments on real
hyperspectral data sets with different noise levels demonstrate that our method
outperforms the state-of-the-art methods significantly. | ['Chunhong Pan', 'Ying Wang', 'Feiyun Zhu', 'Shiming Xiang', 'Bin Fan'] | 2014-03-19 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 4.75940347e-01 -3.12831730e-01 -2.05426678e-01 -1.69788361e-01
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e0bbd597-6497-4c0b-badd-72d47a56c684 | poe-a-panel-of-experts-for-generalized | 2212.08992 | null | https://arxiv.org/abs/2212.08992v1 | https://arxiv.org/pdf/2212.08992v1.pdf | PoE: a Panel of Experts for Generalized Automatic Dialogue Assessment | Chatbots are expected to be knowledgeable across multiple domains, e.g. for daily chit-chat, exchange of information, and grounding in emotional situations. To effectively measure the quality of such conversational agents, a model-based automatic dialogue evaluation metric (ADEM) is expected to perform well across multiple domains. Despite significant progress, an ADEM that works well in one domain does not necessarily generalize to another. This calls for a dedicated network architecture for domain generalization. To tackle the multi-domain dialogue evaluation task, we propose a Panel of Experts (PoE), a multitask network that consists of a shared transformer encoder and a collection of lightweight adapters. The shared encoder captures the general knowledge of dialogues across domains, while each adapter specializes in one specific domain and serves as a domain expert. To validate the idea, we construct a high-quality multi-domain dialogue dataset leveraging data augmentation and pseudo-labeling. The PoE network is comprehensively assessed on 16 dialogue evaluation datasets spanning a wide range of dialogue domains. It achieves state-of-the-art performance in terms of mean Spearman correlation over all the evaluation datasets. It exhibits better zero-shot generalization than existing state-of-the-art ADEMs and the ability to easily adapt to new domains with few-shot transfer learning. | ['Haizhou Li', 'Thomas Friedrichs', 'Qiquan Zhang', "Luis Fernando D'Haro", 'Chen Zhang'] | 2022-12-18 | null | null | null | null | ['general-knowledge', 'dialogue-evaluation'] | ['miscellaneous', 'natural-language-processing'] | [ 3.37620489e-02 3.84782016e-01 -4.92409281e-02 -6.09673858e-01
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f6e6e8a6-53b7-4461-af41-91deb8813756 | transformers-in-speech-processing-a-survey | 2303.11607 | null | https://arxiv.org/abs/2303.11607v1 | https://arxiv.org/pdf/2303.11607v1.pdf | Transformers in Speech Processing: A Survey | The remarkable success of transformers in the field of natural language processing has sparked the interest of the speech-processing community, leading to an exploration of their potential for modeling long-range dependencies within speech sequences. Recently, transformers have gained prominence across various speech-related domains, including automatic speech recognition, speech synthesis, speech translation, speech para-linguistics, speech enhancement, spoken dialogue systems, and numerous multimodal applications. In this paper, we present a comprehensive survey that aims to bridge research studies from diverse subfields within speech technology. By consolidating findings from across the speech technology landscape, we provide a valuable resource for researchers interested in harnessing the power of transformers to advance the field. We identify the challenges encountered by transformers in speech processing while also offering insights into potential solutions to address these issues. | ['Junaid Qadir', 'Moazzam Shoukat', 'Fahad Shamshad', 'Heriberto Cuayahuitl', 'Aun Zaidi', 'Siddique Latif'] | 2023-03-21 | null | null | null | null | ['spoken-dialogue-systems', 'speech-enhancement', 'speech-synthesis'] | ['speech', 'speech', 'speech'] | [ 5.61361969e-01 2.25574866e-01 -1.73520684e-01 -5.88895142e-01
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96ff5762-89c0-4e8d-a413-fcdc28c99086 | routing-enforced-generative-model-for-recipe | null | null | https://aclanthology.org/2020.emnlp-main.311 | https://aclanthology.org/2020.emnlp-main.311.pdf | Routing Enforced Generative Model for Recipe Generation | One of the most challenging part of recipe generation is to deal with the complex restrictions among the input ingredients. Previous researches simplify the problem by treating the inputs independently and generating recipes containing as much information as possible. In this work, we propose a routing method to dive into the content selection under the internal restrictions. The routing enforced generative model (RGM) can generate appropriate recipes according to the given ingredients and user preferences. Our model yields new state-of-the-art results on the recipe generation task with significant improvements on BLEU, F1 and human evaluation. | ['Xiaojun Wan', 'Hongyu Zang', 'Zhiwei Yu'] | null | null | null | null | emnlp-2020-11 | ['recipe-generation'] | ['miscellaneous'] | [ 1.30157143e-01 -2.51321308e-02 -2.69617051e-01 -3.98627222e-01
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3f935501-6756-48bf-bdce-85b6d8a99b1b | rdd2022-a-multi-national-image-dataset-for | 2209.08538 | null | https://arxiv.org/abs/2209.08538v1 | https://arxiv.org/pdf/2209.08538v1.pdf | RDD2022: A multi-national image dataset for automatic Road Damage Detection | The data article describes the Road Damage Dataset, RDD2022, which comprises 47,420 road images from six countries, Japan, India, the Czech Republic, Norway, the United States, and China. The images have been annotated with more than 55,000 instances of road damage. Four types of road damage, namely longitudinal cracks, transverse cracks, alligator cracks, and potholes, are captured in the dataset. The annotated dataset is envisioned for developing deep learning-based methods to detect and classify road damage automatically. The dataset has been released as a part of the Crowd sensing-based Road Damage Detection Challenge (CRDDC2022). The challenge CRDDC2022 invites researchers from across the globe to propose solutions for automatic road damage detection in multiple countries. The municipalities and road agencies may utilize the RDD2022 dataset, and the models trained using RDD2022 for low-cost automatic monitoring of road conditions. Further, computer vision and machine learning researchers may use the dataset to benchmark the performance of different algorithms for other image-based applications of the same type (classification, object detection, etc.). | ['Yoshihide Sekimoto', 'Durga Toshniwal', 'Sanjay Kumar Ghosh', 'Hiroya Maeda', 'Deeksha Arya'] | 2022-09-18 | null | null | null | null | ['road-damage-detection'] | ['computer-vision'] | [-1.74128041e-01 1.38647184e-01 1.44367367e-01 3.07711177e-02
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c2abf829-3838-4007-b568-39a88f0aa183 | unsupervised-learning-based-depth-estimation | 1901.07288 | null | http://arxiv.org/abs/1901.07288v1 | http://arxiv.org/pdf/1901.07288v1.pdf | Unsupervised Learning-based Depth Estimation aided Visual SLAM Approach | The RGB-D camera maintains a limited range for working and is hard to
accurately measure the depth information in a far distance. Besides, the RGB-D
camera will easily be influenced by strong lighting and other external factors,
which will lead to a poor accuracy on the acquired environmental depth
information. Recently, deep learning technologies have achieved great success
in the visual SLAM area, which can directly learn high-level features from the
visual inputs and improve the estimation accuracy of the depth information.
Therefore, deep learning technologies maintain the potential to extend the
source of the depth information and improve the performance of the SLAM system.
However, the existing deep learning-based methods are mainly supervised and
require a large amount of ground-truth depth data, which is hard to acquire
because of the realistic constraints. In this paper, we first present an
unsupervised learning framework, which not only uses image reconstruction for
supervising but also exploits the pose estimation method to enhance the
supervised signal and add training constraints for the task of monocular depth
and camera motion estimation. Furthermore, we successfully exploit our
unsupervised learning framework to assist the traditional ORB-SLAM system when
the initialization module of ORB-SLAM method could not match enough features.
Qualitative and quantitative experiments have shown that our unsupervised
learning framework performs the depth estimation task comparable to the
supervised methods and outperforms the previous state-of-the-art approach by
$13.5\%$ on KITTI dataset. Besides, our unsupervised learning framework could
significantly accelerate the initialization process of ORB-SLAM system and
effectively improve the accuracy on environmental mapping in strong lighting
and weak texture scenes. | ['Huaimin Wang', 'Bo Ding', 'Suning Shang', 'Pengfei Zhang', 'Lei Zhang', 'Mingyang Geng'] | 2019-01-22 | null | null | null | null | ['depth-and-camera-motion'] | ['computer-vision'] | [-7.73435533e-02 -3.77227634e-01 -3.67109060e-01 -5.65210640e-01
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d4148412-661c-4e2a-9980-191b3d7becb7 | humman-multi-modal-4d-human-dataset-for | 2204.13686 | null | https://arxiv.org/abs/2204.13686v2 | https://arxiv.org/pdf/2204.13686v2.pdf | HuMMan: Multi-Modal 4D Human Dataset for Versatile Sensing and Modeling | 4D human sensing and modeling are fundamental tasks in vision and graphics with numerous applications. With the advances of new sensors and algorithms, there is an increasing demand for more versatile datasets. In this work, we contribute HuMMan, a large-scale multi-modal 4D human dataset with 1000 human subjects, 400k sequences and 60M frames. HuMMan has several appealing properties: 1) multi-modal data and annotations including color images, point clouds, keypoints, SMPL parameters, and textured meshes; 2) popular mobile device is included in the sensor suite; 3) a set of 500 actions, designed to cover fundamental movements; 4) multiple tasks such as action recognition, pose estimation, parametric human recovery, and textured mesh reconstruction are supported and evaluated. Extensive experiments on HuMMan voice the need for further study on challenges such as fine-grained action recognition, dynamic human mesh reconstruction, point cloud-based parametric human recovery, and cross-device domain gaps. | ['Ziwei Liu', 'Lei Yang', 'Chen Change Loy', 'Mingyuan Zhang', 'Fangzhou Hong', 'Liang Pan', 'Yifan Yu', 'Yang Gao', 'Xiangyu Fan', 'Wenjia Wang', 'Tao Yu', 'Zhengyu Lin', 'Ailing Zeng', 'Daxuan Ren', 'Zhongang Cai'] | 2022-04-28 | null | null | null | null | ['fine-grained-action-recognition'] | ['computer-vision'] | [ 1.72112927e-01 -4.96412188e-01 2.37318408e-03 -5.57107590e-02
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4229bfd1-8c6a-423c-800c-089596383b3a | generative-job-recommendations-with-large | 2307.02157 | null | https://arxiv.org/abs/2307.02157v1 | https://arxiv.org/pdf/2307.02157v1.pdf | Generative Job Recommendations with Large Language Model | The rapid development of online recruitment services has encouraged the utilization of recommender systems to streamline the job seeking process. Predominantly, current job recommendations deploy either collaborative filtering or person-job matching strategies. However, these models tend to operate as "black-box" systems and lack the capacity to offer explainable guidance to job seekers. Moreover, conventional matching-based recommendation methods are limited to retrieving and ranking existing jobs in the database, restricting their potential as comprehensive career AI advisors. To this end, here we present GIRL (GeneratIve job Recommendation based on Large language models), a novel approach inspired by recent advancements in the field of Large Language Models (LLMs). We initially employ a Supervised Fine-Tuning (SFT) strategy to instruct the LLM-based generator in crafting suitable Job Descriptions (JDs) based on the Curriculum Vitae (CV) of a job seeker. Moreover, we propose to train a model which can evaluate the matching degree between CVs and JDs as a reward model, and we use Proximal Policy Optimization (PPO)-based Reinforcement Learning (RL) method to further fine-tine the generator. This aligns the generator with recruiter feedback, tailoring the output to better meet employer preferences. In particular, GIRL serves as a job seeker-centric generative model, providing job suggestions without the need of a candidate set. This capability also enhances the performance of existing job recommendation models by supplementing job seeking features with generated content. With extensive experiments on a large-scale real-world dataset, we demonstrate the substantial effectiveness of our approach. We believe that GIRL introduces a paradigm-shifting approach to job recommendation systems, fostering a more personalized and comprehensive job-seeking experience. | ['Hui Xiong', 'HengShu Zhu', 'Likang Wu', 'Xiao Hu', 'Zhaopeng Qiu', 'Zhi Zheng'] | 2023-07-05 | null | null | null | null | ['reinforcement-learning-1', 'collaborative-filtering'] | ['methodology', 'miscellaneous'] | [ 1.13237716e-01 4.11416382e-01 -7.19348550e-01 -6.07162893e-01
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d2480161-86ba-4b83-ade1-92ac4e532f7c | suppress-and-balance-a-simple-gated-network | 2007.08074 | null | https://arxiv.org/abs/2007.08074v3 | https://arxiv.org/pdf/2007.08074v3.pdf | Suppress and Balance: A Simple Gated Network for Salient Object Detection | Most salient object detection approaches use U-Net or feature pyramid networks (FPN) as their basic structures. These methods ignore two key problems when the encoder exchanges information with the decoder: one is the lack of interference control between them, the other is without considering the disparity of the contributions of different encoder blocks. In this work, we propose a simple gated network (GateNet) to solve both issues at once. With the help of multilevel gate units, the valuable context information from the encoder can be optimally transmitted to the decoder. We design a novel gated dual branch structure to build the cooperation among different levels of features and improve the discriminability of the whole network. Through the dual branch design, more details of the saliency map can be further restored. In addition, we adopt the atrous spatial pyramid pooling based on the proposed "Fold" operation (Fold-ASPP) to accurately localize salient objects of various scales. Extensive experiments on five challenging datasets demonstrate that the proposed model performs favorably against most state-of-the-art methods under different evaluation metrics. | ['Youwei Pang', 'Lihe Zhang', 'Lei Zhang', 'Huchuan Lu', 'Xiaoqi Zhao'] | 2020-07-16 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2852_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123470035.pdf | eccv-2020-8 | ['dichotomous-image-segmentation'] | ['computer-vision'] | [ 1.57200083e-01 -6.91480096e-03 -4.77968715e-02 -1.41083658e-01
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7d22d115-67f0-4cb0-b916-2dc948ab165c | on-bonus-based-exploration-methods-in-the | null | null | https://openreview.net/forum?id=BJewlyStDr | https://openreview.net/pdf?id=BJewlyStDr | On Bonus Based Exploration Methods In The Arcade Learning Environment | Research on exploration in reinforcement learning, as applied to Atari 2600 game-playing, has emphasized tackling difficult exploration problems such as Montezuma's Revenge (Bellemare et al., 2016). Recently, bonus-based exploration methods, which explore by augmenting the environment reward, have reached above-human average performance on such domains. In this paper we reassess popular bonus-based exploration methods within a common evaluation framework. We combine Rainbow (Hessel et al., 2018) with different exploration bonuses and evaluate its performance on Montezuma's Revenge, Bellemare et al.'s set of hard of exploration games with sparse rewards, and the whole Atari 2600 suite. We find that while exploration bonuses lead to higher score on Montezuma's Revenge they do not provide meaningful gains over the simpler epsilon-greedy scheme. In fact, we find that methods that perform best on that game often underperform epsilon-greedy on easy exploration Atari 2600 games. We find that our conclusions remain valid even when hyperparameters are tuned for these easy-exploration games. Finally, we find that none of the methods surveyed benefit from additional training samples (1 billion frames, versus Rainbow's 200 million) on Bellemare et al.'s hard exploration games. Our results suggest that recent gains in Montezuma's Revenge may be better attributed to architecture change, rather than better exploration schemes; and that the real pace of progress in exploration research for Atari 2600 games may have been obfuscated by good results on a single domain. | ['Aaron Courville', 'William Fedus', 'Marc G. Bellemare', 'Adrien Ali Taiga', 'Marlos C. Machado'] | 2020-01-01 | null | null | null | iclr-2020-1 | ['montezumas-revenge'] | ['playing-games'] | [-4.88501638e-01 1.19107075e-01 -1.45592138e-01 2.03883186e-01
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5cfed957-ffda-4af2-883d-1dad47816db9 | vista-vision-transformer-enhanced-by-u-net | 2204.11024 | null | https://arxiv.org/abs/2204.11024v1 | https://arxiv.org/pdf/2204.11024v1.pdf | VISTA: Vision Transformer enhanced by U-Net and Image Colorfulness Frame Filtration for Automatic Retail Checkout | Multi-class product counting and recognition identifies product items from images or videos for automated retail checkout. The task is challenging due to the real-world scenario of occlusions where product items overlap, fast movement in the conveyor belt, large similarity in overall appearance of the items being scanned, novel products, and the negative impact of misidentifying items. Further, there is a domain bias between training and test sets, specifically, the provided training dataset consists of synthetic images and the test set videos consist of foreign objects such as hands and tray. To address these aforementioned issues, we propose to segment and classify individual frames from a video sequence. The segmentation method consists of a unified single product item- and hand-segmentation followed by entropy masking to address the domain bias problem. The multi-class classification method is based on Vision Transformers (ViT). To identify the frames with target objects, we utilize several image processing methods and propose a custom metric to discard frames not having any product items. Combining all these mechanisms, our best system achieves 3rd place in the AI City Challenge 2022 Track 4 with an F1 score of 0.4545. Code will be available at | ['Nabeel Mohammed', 'Labiba Kanij Rupty', 'Hasib Zunair', 'Nazia Tasnim', 'Md. Istiak Hossain Shihab'] | 2022-04-23 | null | null | null | null | ['hand-segmentation'] | ['computer-vision'] | [ 4.75254536e-01 -4.17579025e-01 -2.74029136e-01 -3.83114427e-01
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d660b72b-0fe0-4adb-b8f2-ba7ac37c74b1 | simultaneous-adversarial-attacks-on-multiple | 2304.05048 | null | https://arxiv.org/abs/2304.05048v1 | https://arxiv.org/pdf/2304.05048v1.pdf | Simultaneous Adversarial Attacks On Multiple Face Recognition System Components | In this work, we investigate the potential threat of adversarial examples to the security of face recognition systems. Although previous research has explored the adversarial risk to individual components of FRSs, our study presents an initial exploration of an adversary simultaneously fooling multiple components: the face detector and feature extractor in an FRS pipeline. We propose three multi-objective attacks on FRSs and demonstrate their effectiveness through a preliminary experimental analysis on a target system. Our attacks achieved up to 100% Attack Success Rates against both the face detector and feature extractor and were able to manipulate the face detection probability by up to 50% depending on the adversarial objective. This research identifies and examines novel attack vectors against FRSs and suggests possible ways to augment the robustness by leveraging the attack vector's knowledge during training of an FRS's components. | ['Toshinori Araki', 'Kazuya Kakizaki', 'Inderjeet Singh'] | 2023-04-11 | null | null | null | null | ['face-detection'] | ['computer-vision'] | [ 4.96178478e-01 3.96617740e-01 5.29145598e-01 -3.22450608e-01
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85e295e2-edb9-4cb2-b7b9-f9878e718c32 | deep-learning-for-video-game-playing | 1708.07902 | null | http://arxiv.org/abs/1708.07902v3 | http://arxiv.org/pdf/1708.07902v3.pdf | Deep Learning for Video Game Playing | In this article, we review recent Deep Learning advances in the context of
how they have been applied to play different types of video games such as
first-person shooters, arcade games, and real-time strategy games. We analyze
the unique requirements that different game genres pose to a deep learning
system and highlight important open challenges in the context of applying these
machine learning methods to video games, such as general game playing, dealing
with extremely large decision spaces and sparse rewards. | ['Sebastian Risi', 'Niels Justesen', 'Julian Togelius', 'Philip Bontrager'] | 2017-08-25 | null | null | null | null | ['real-time-strategy-games'] | ['playing-games'] | [-2.37715185e-01 -1.58584654e-01 -1.08272962e-01 8.18636045e-02
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0d4afba7-be39-43ab-a167-e25d8351416d | application-of-graph-based-features-in | 2205.08467 | null | https://arxiv.org/abs/2205.08467v1 | https://arxiv.org/pdf/2205.08467v1.pdf | Application of Graph Based Features in Computer Aided Diagnosis for Histopathological Image Classification of Gastric Cancer | The gold standard for gastric cancer detection is gastric histopathological image analysis, but there are certain drawbacks in the existing histopathological detection and diagnosis. In this paper, based on the study of computer aided diagnosis system, graph based features are applied to gastric cancer histopathology microscopic image analysis, and a classifier is used to classify gastric cancer cells from benign cells. Firstly, image segmentation is performed, and after finding the region, cell nuclei are extracted using the k-means method, the minimum spanning tree (MST) is drawn, and graph based features of the MST are extracted. The graph based features are then put into the classifier for classification. In this study, different segmentation methods are compared in the tissue segmentation stage, among which are Level-Set, Otsu thresholding, watershed, SegNet, U-Net and Trans-U-Net segmentation; Graph based features, Red, Green, Blue features, Grey-Level Co-occurrence Matrix features, Histograms of Oriented Gradient features and Local Binary Patterns features are compared in the feature extraction stage; Radial Basis Function (RBF) Support Vector Machine (SVM), Linear SVM, Artificial Neural Network, Random Forests, k-NearestNeighbor, VGG16, and Inception-V3 are compared in the classifier stage. It is found that using U-Net to segment tissue areas, then extracting graph based features, and finally using RBF SVM classifier gives the optimal results with 94.29%. | ['Marcin Grzegorzek', 'Xinyu Huang', 'Hongzan Sun', 'Xiaoyan Li', 'Yixin Li', 'Yuchao Zheng', 'HaoYuan Chen', 'Shiliang Ai', 'Chen Li', 'Haiqing Zhang'] | 2022-05-17 | null | null | null | null | ['histopathological-image-classification'] | ['medical'] | [ 1.08220756e-01 -1.37934670e-01 -3.75458121e-01 1.43276989e-01
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ab99e497-877c-42a2-ba52-314cc720d996 | domain-enhanced-arbitrary-image-style | 2205.09542 | null | https://arxiv.org/abs/2205.09542v2 | https://arxiv.org/pdf/2205.09542v2.pdf | Domain Enhanced Arbitrary Image Style Transfer via Contrastive Learning | In this work, we tackle the challenging problem of arbitrary image style transfer using a novel style feature representation learning method. A suitable style representation, as a key component in image stylization tasks, is essential to achieve satisfactory results. Existing deep neural network based approaches achieve reasonable results with the guidance from second-order statistics such as Gram matrix of content features. However, they do not leverage sufficient style information, which results in artifacts such as local distortions and style inconsistency. To address these issues, we propose to learn style representation directly from image features instead of their second-order statistics, by analyzing the similarities and differences between multiple styles and considering the style distribution. Specifically, we present Contrastive Arbitrary Style Transfer (CAST), which is a new style representation learning and style transfer method via contrastive learning. Our framework consists of three key components, i.e., a multi-layer style projector for style code encoding, a domain enhancement module for effective learning of style distribution, and a generative network for image style transfer. We conduct qualitative and quantitative evaluations comprehensively to demonstrate that our approach achieves significantly better results compared to those obtained via state-of-the-art methods. Code and models are available at https://github.com/zyxElsa/CAST_pytorch | ['Changsheng Xu', 'Tong-Yee Lee', 'Chongyang Ma', 'Haibin Huang', 'WeiMing Dong', 'Fan Tang', 'Yuxin Zhang'] | 2022-05-19 | null | null | null | null | ['image-stylization'] | ['computer-vision'] | [ 2.90730417e-01 -5.24703681e-01 -5.05465316e-03 -4.18854237e-01
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7857715e-b2e4-4509-bebd-3454e5d2c5f4 | hierarchical-document-encoder-for-parallel | 1906.08401 | null | https://arxiv.org/abs/1906.08401v2 | https://arxiv.org/pdf/1906.08401v2.pdf | Hierarchical Document Encoder for Parallel Corpus Mining | We explore using multilingual document embeddings for nearest neighbor mining of parallel data. Three document-level representations are investigated: (i) document embeddings generated by simply averaging multilingual sentence embeddings; (ii) a neural bag-of-words (BoW) document encoding model; (iii) a hierarchical multilingual document encoder (HiDE) that builds on our sentence-level model. The results show document embeddings derived from sentence-level averaging are surprisingly effective for clean datasets, but suggest models trained hierarchically at the document-level are more effective on noisy data. Analysis experiments demonstrate our hierarchical models are very robust to variations in the underlying sentence embedding quality. Using document embeddings trained with HiDE achieves state-of-the-art performance on United Nations (UN) parallel document mining, 94.9% P@1 for en-fr and 97.3% P@1 for en-es. | ['Yun-Hsuan Sung', 'Yinfei Yang', 'Brian Strope', 'Daniel Cer', 'Mandy Guo', 'Heming Ge', 'Ray Kurzweil', 'Keith Stevens'] | 2019-06-20 | hierarchical-document-encoder-for-parallel-1 | https://aclanthology.org/W19-5207 | https://aclanthology.org/W19-5207.pdf | ws-2019-8 | ['parallel-corpus-mining'] | ['natural-language-processing'] | [-1.61451846e-01 -4.36769314e-02 -2.96839237e-01 -3.78242671e-01
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935e34ba-1c51-4b09-8ea6-72e12ab94a17 | polarimetric-inverse-rendering-for | 2208.11836 | null | https://arxiv.org/abs/2208.11836v1 | https://arxiv.org/pdf/2208.11836v1.pdf | Polarimetric Inverse Rendering for Transparent Shapes Reconstruction | In this work, we propose a novel method for the detailed reconstruction of transparent objects by exploiting polarimetric cues. Most of the existing methods usually lack sufficient constraints and suffer from the over-smooth problem. Hence, we introduce polarization information as a complementary cue. We implicitly represent the object's geometry as a neural network, while the polarization render is capable of rendering the object's polarization images from the given shape and illumination configuration. Direct comparison of the rendered polarization images to the real-world captured images will have additional errors due to the transmission in the transparent object. To address this issue, the concept of reflection percentage which represents the proportion of the reflection component is introduced. The reflection percentage is calculated by a ray tracer and then used for weighting the polarization loss. We build a polarization dataset for multi-view transparent shapes reconstruction to verify our method. The experimental results show that our method is capable of recovering detailed shapes and improving the reconstruction quality of transparent objects. Our dataset and code will be publicly available at https://github.com/shaomq2187/TransPIR. | ['Xueqian Wang', 'Dongxu Duan', 'Chongkun Xia', 'Mingqi Shao'] | 2022-08-25 | null | null | null | null | ['transparent-objects'] | ['computer-vision'] | [ 1.36910975e-01 -1.34418726e-01 2.05242589e-01 -3.10844928e-01
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3c0ab466-a98e-4925-b2d5-cda9d48bf2e5 | modeling-label-semantics-improves-activity | 2301.03462 | null | https://arxiv.org/abs/2301.03462v1 | https://arxiv.org/pdf/2301.03462v1.pdf | Modeling Label Semantics Improves Activity Recognition | Human activity recognition (HAR) aims to classify sensory time series into different activities, with wide applications in activity tracking, healthcare, human computer interaction, etc. Existing HAR works improve recognition performance by designing more complicated feature extraction methods, but they neglect the label semantics by simply treating labels as integer IDs. We find that many activities in the current HAR datasets have shared label names, e.g., "open door" and "open fridge", "walk upstairs" and "walk downstairs". Through some exploratory analysis, we find that such shared structure in activity names also maps to similarity in the input features. To this end, we design a sequence-to-sequence framework to decode the label name semantics rather than classifying labels as integer IDs. Our proposed method decomposes learning activities into learning shared tokens ("open", "walk"), which is easier than learning the joint distribution ("open fridge", "walk upstairs") and helps transfer learning to activities with insufficient data samples. For datasets originally without shared tokens in label names, we also offer an automated method, using OpenAI's ChatGPT, to generate shared actions and objects. Extensive experiments on seven HAR benchmark datasets demonstrate the state-of-the-art performance of our method. We also show better performance in the long-tail activity distribution settings and few-shot settings. | ['Jingbo Shang', 'Rajesh K. Gupta', 'Dezhi Hong', 'Ranak Roy Chowdhury', 'Xiyuan Zhang'] | 2023-01-01 | null | null | null | null | ['human-activity-recognition', 'human-activity-recognition'] | ['computer-vision', 'time-series'] | [ 3.05712730e-01 -4.37552243e-01 -5.24568260e-01 -4.91947949e-01
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30885e41-2001-4d76-bce9-fbad23459fba | towards-a-design-framework-for-tnn-based | 2205.14248 | null | https://arxiv.org/abs/2205.14248v1 | https://arxiv.org/pdf/2205.14248v1.pdf | Towards a Design Framework for TNN-Based Neuromorphic Sensory Processing Units | Temporal Neural Networks (TNNs) are spiking neural networks that exhibit brain-like sensory processing with high energy efficiency. This work presents the ongoing research towards developing a custom design framework for designing efficient application-specific TNN-based Neuromorphic Sensory Processing Units (NSPUs). This paper examines previous works on NSPU designs for UCR time-series clustering and MNIST image classification applications. Current ideas for a custom design framework and tools that enable efficient software-to-hardware design flow for rapid design space exploration of application-specific NSPUs while leveraging EDA tools to obtain post-layout netlist and power-performance-area (PPA) metrics are described. Future research directions are also outlined. | ['John Paul Shen', 'Prabhu Vellaisamy'] | 2022-05-27 | null | null | null | null | ['time-series-clustering'] | ['time-series'] | [ 5.81914008e-01 -5.52461743e-01 6.11542724e-02 -5.04633367e-01
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b2088864-d283-4eba-a543-af668798af97 | identification-and-validation-of | 2107.02905 | null | https://arxiv.org/abs/2107.02905v2 | https://arxiv.org/pdf/2107.02905v2.pdf | An in silico drug repurposing pipeline to identify drugs with the potential to inhibit SARS-CoV-2 replication | Drug repurposing provides an opportunity to redeploy drugs, which ideally are already approved for use in humans, for the treatment of other diseases. For example, the repurposing of dexamethasone and baricitinib has played a crucial role in saving patient lives during the ongoing SARS-CoV-2 pandemic. There remains a need to expand therapeutic approaches to prevent life-threatening complications in patients with COVID-19. Using an in silico approach based on structural similarity to drugs already in clinical trials for COVID-19, potential drugs were predicted for repurposing. For a subset of identified drugs with different targets to their corresponding COVID-19 clinical trial drug, a mechanism of action analysis was applied to establish whether they might have a role in inhibiting the replication of SARS-CoV-2. Of sixty drugs predicted in this study, two with the potential to inhibit SARS-CoV-2 replication were identified using mechanism of action analysis. Triamcinolone is a corticosteroid that is structurally similar to dexamethasone; gallopamil is a calcium channel blocker that is structurally similar to verapamil. In silico approaches indicate possible mechanisms of action for both drugs in inhibiting SARS-CoV-2 replication. The identification of these drugs as potentially useful for patients with COVID-19 who are at a higher risk of developing severe disease supports the use of in silico approaches to facilitate quick and cost-effective drug repurposing. Such drugs could expand the number of treatments available to patients who are not protected by vaccination. | ['Namshik Han', 'Vasanthi Priyadarshini Gaddi', 'Paul Bilokon', 'Mukunthan Tharmakulasingam', 'Justin Barton', 'Alexandre Abraham', 'Eoghan MacMahon', 'Soorin Yim', 'Woochang Hwang', 'Méabh MacMahon'] | 2021-07-05 | null | null | null | null | ['action-analysis'] | ['computer-vision'] | [ 3.95815015e-01 -4.13024366e-01 9.48778242e-02 5.52440016e-03
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fce976fb-12c3-405c-95e7-33203afa4bea | a-bert-based-distractor-generation-scheme | 2010.05384 | null | https://arxiv.org/abs/2010.05384v1 | https://arxiv.org/pdf/2010.05384v1.pdf | A BERT-based Distractor Generation Scheme with Multi-tasking and Negative Answer Training Strategies | In this paper, we investigate the following two limitations for the existing distractor generation (DG) methods. First, the quality of the existing DG methods are still far from practical use. There is still room for DG quality improvement. Second, the existing DG designs are mainly for single distractor generation. However, for practical MCQ preparation, multiple distractors are desired. Aiming at these goals, in this paper, we present a new distractor generation scheme with multi-tasking and negative answer training strategies for effectively generating \textit{multiple} distractors. The experimental results show that (1) our model advances the state-of-the-art result from 28.65 to 39.81 (BLEU 1 score) and (2) the generated multiple distractors are diverse and show strong distracting power for multiple choice question. | ['Yao-Chung Fan', 'Ying-Hong Chan', 'Ho-Lam Chung'] | 2020-10-12 | null | null | null | null | ['distractor-generation'] | ['natural-language-processing'] | [-2.38253787e-01 -1.45325214e-01 -2.61324972e-01 1.23441607e-01
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6cde0dcc-d2d5-46cc-a7cb-5c5e3199e942 | 2305-15017 | 2305.15017 | null | https://arxiv.org/abs/2305.15017v1 | https://arxiv.org/pdf/2305.15017v1.pdf | Calc-X: Enriching Arithmetical Chain-of-Thoughts Datasets by Interaction with Symbolic Systems | This report overviews our ongoing work in enriching chain-of-thoughts datasets requiring arithmetical reasoning with the integration of non-parametric components, such as a calculator. We conduct an analysis of prominent relevant datasets such as GSM8K, Ape210K, AQuA-RAT, and MathQA and propose a machine-processable HTML-like format specifically tailored for working with semi-structured chains. By converting the datasets into this unified format, we enable the effective integration of large language models and symbolic systems, empowering them to tackle arithmetical reasoning tasks more efficiently. | ['Michal Štefánik', 'Marek Kadlčík'] | 2023-05-24 | null | null | null | null | ['gsm8k'] | ['natural-language-processing'] | [-6.04434252e-01 4.04605567e-01 -8.32034573e-02 -6.58823013e-01
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f207eddd-a34f-4793-b0a4-5054b8308906 | multiple-instance-dictionary-learning-for | 1706.03373 | null | http://arxiv.org/abs/1706.03373v2 | http://arxiv.org/pdf/1706.03373v2.pdf | Multiple Instance Dictionary Learning for Beat-to-Beat Heart Rate Monitoring from Ballistocardiograms | A multiple instance dictionary learning approach, Dictionary Learning using
Functions of Multiple Instances (DL-FUMI), is used to perform beat-to-beat
heart rate estimation and to characterize heartbeat signatures from
ballistocardiogram (BCG) signals collected with a hydraulic bed sensor. DL-FUMI
estimates a "heartbeat concept" that represents an individual's personal
ballistocardiogram heartbeat pattern. DL-FUMI formulates heartbeat detection
and heartbeat characterization as a multiple instance learning problem to
address the uncertainty inherent in aligning BCG signals with ground truth
during training. Experimental results show that the estimated heartbeat concept
found by DL-FUMI is an effective heartbeat prototype and achieves superior
performance over comparison algorithms. | ['Alina Zare', 'Princess Lyons', 'Bo-Yu Su', 'Changzhe Jiao', 'Marjorie Skubic', 'K. C. Ho'] | 2017-06-11 | null | null | null | null | ['heart-rate-estimation'] | ['medical'] | [ 3.49492878e-01 7.02092871e-02 -3.83557081e-01 -1.84033215e-01
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12798034-4a84-4909-8b03-366ef5c2d591 | automatic-model-selection-with-large-language | 2305.14333 | null | https://arxiv.org/abs/2305.14333v1 | https://arxiv.org/pdf/2305.14333v1.pdf | Automatic Model Selection with Large Language Models for Reasoning | Chain-of-Thought and Program-Aided Language Models represent two distinct reasoning methods, each with its own strengths and weaknesses. We demonstrate that it is possible to combine the best of both worlds by using different models for different problems, employing a large language model (LLM) to perform model selection. Through a theoretical analysis, we discover that the performance improvement is determined by the differences between the combined methods and the success rate of choosing the correct model. On eight reasoning datasets, our proposed approach shows significant improvements. Furthermore, we achieve new state-of-the-art results on GSM8K and SVAMP with accuracies of 96.5% and 93.7%, respectively. Our code is publicly available at https://github.com/XuZhao0/Model-Selection-Reasoning. | ['Qizhe Xie', 'Junxian He', 'Kenji Kawaguchi', 'Yuxi Xie', 'Xu Zhao'] | 2023-05-23 | null | null | null | null | ['math-word-problem-solving', 'gsm8k', 'arithmetic-reasoning', 'math-word-problem-solving', 'math-word-problem-solving'] | ['knowledge-base', 'natural-language-processing', 'reasoning', 'reasoning', 'time-series'] | [-1.13622129e-01 -6.37566075e-02 -3.79974842e-01 -2.88214594e-01
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b802dda3-9bcf-4857-a8cb-59f082db7e32 | sad-semi-supervised-anomaly-detection-on | 2305.13573 | null | https://arxiv.org/abs/2305.13573v1 | https://arxiv.org/pdf/2305.13573v1.pdf | SAD: Semi-Supervised Anomaly Detection on Dynamic Graphs | Anomaly detection aims to distinguish abnormal instances that deviate significantly from the majority of benign ones. As instances that appear in the real world are naturally connected and can be represented with graphs, graph neural networks become increasingly popular in tackling the anomaly detection problem. Despite the promising results, research on anomaly detection has almost exclusively focused on static graphs while the mining of anomalous patterns from dynamic graphs is rarely studied but has significant application value. In addition, anomaly detection is typically tackled from semi-supervised perspectives due to the lack of sufficient labeled data. However, most proposed methods are limited to merely exploiting labeled data, leaving a large number of unlabeled samples unexplored. In this work, we present semi-supervised anomaly detection (SAD), an end-to-end framework for anomaly detection on dynamic graphs. By a combination of a time-equipped memory bank and a pseudo-label contrastive learning module, SAD is able to fully exploit the potential of large unlabeled samples and uncover underlying anomalies on evolving graph streams. Extensive experiments on four real-world datasets demonstrate that SAD efficiently discovers anomalies from dynamic graphs and outperforms existing advanced methods even when provided with only little labeled data. | ['Liang Chen', 'Tianyi Zhang', 'Changhua Meng', 'Bowen Song', 'Baokun Wang', 'Xiaolong Xu', 'Wenlong Zhao', 'Jintang Li', 'Jihai Dong', 'Sheng Tian'] | 2023-05-23 | null | null | null | null | ['supervised-anomaly-detection', 'semi-supervised-anomaly-detection', 'pseudo-label'] | ['computer-vision', 'computer-vision', 'miscellaneous'] | [ 2.96357721e-01 8.50184560e-02 -9.73231122e-02 -2.22277895e-01
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5710b814-d439-44a0-9024-e82b86482b63 | enhancing-keyphrase-extraction-from-long | 2305.09316 | null | https://arxiv.org/abs/2305.09316v1 | https://arxiv.org/pdf/2305.09316v1.pdf | Enhancing Keyphrase Extraction from Long Scientific Documents using Graph Embeddings | In this study, we investigate using graph neural network (GNN) representations to enhance contextualized representations of pre-trained language models (PLMs) for keyphrase extraction from lengthy documents. We show that augmenting a PLM with graph embeddings provides a more comprehensive semantic understanding of words in a document, particularly for long documents. We construct a co-occurrence graph of the text and embed it using a graph convolutional network (GCN) trained on the task of edge prediction. We propose a graph-enhanced sequence tagging architecture that augments contextualized PLM embeddings with graph representations. Evaluating on benchmark datasets, we demonstrate that enhancing PLMs with graph embeddings outperforms state-of-the-art models on long documents, showing significant improvements in F1 scores across all the datasets. Our study highlights the potential of GNN representations as a complementary approach to improve PLM performance for keyphrase extraction from long documents. | ['José Portela', 'Alvaro J. López-López', 'Debanjan Mahata', 'Roberto Martínez-Cruz'] | 2023-05-16 | null | null | null | null | ['keyphrase-extraction'] | ['natural-language-processing'] | [ 2.46967778e-01 4.60029632e-01 -5.73969543e-01 2.82261610e-01
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a7cca79d-f4d7-4f1c-901c-b7b0d8739fb6 | prefix-tuning-for-automated-audio-captioning | 2303.17489 | null | https://arxiv.org/abs/2303.17489v2 | https://arxiv.org/pdf/2303.17489v2.pdf | Prefix tuning for automated audio captioning | Audio captioning aims to generate text descriptions from environmental sounds. One challenge of audio captioning is the difficulty of the generalization due to the lack of audio-text paired training data. In this work, we propose a simple yet effective method of dealing with small-scaled datasets by leveraging a pre-trained language model. We keep the language model frozen to maintain the expressivity for text generation, and we only learn to extract global and temporal features from the input audio. To bridge a modality gap between the audio features and the language model, we employ mapping networks that translate audio features to the continuous vectors the language model can understand, called prefixes. We evaluate our proposed method on the Clotho and AudioCaps dataset and show our method outperforms prior arts in diverse experimental settings. | ['Kim Sung-Bin', 'Tae-Hyun Oh', 'Minkyu Kim'] | 2023-03-30 | null | null | null | null | ['audio-captioning'] | ['audio'] | [ 4.79166746e-01 1.76537350e-01 8.07428136e-02 -2.19889715e-01
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3a47af48-9fca-4441-81d6-7e9698e934a5 | graphon-estimation-in-bipartite-graphs-with | 2304.03590 | null | https://arxiv.org/abs/2304.03590v1 | https://arxiv.org/pdf/2304.03590v1.pdf | Graphon Estimation in bipartite graphs with observable edge labels and unobservable node labels | Many real-world data sets can be presented in the form of a matrix whose entries correspond to the interaction between two entities of different natures (number of times a web user visits a web page, a student's grade in a subject, a patient's rating of a doctor, etc.). We assume in this paper that the mentioned interaction is determined by unobservable latent variables describing each entity. Our objective is to estimate the conditional expectation of the data matrix given the unobservable variables. This is presented as a problem of estimation of a bivariate function referred to as graphon. We study the cases of piecewise constant and H\"older-continuous graphons. We establish finite sample risk bounds for the least squares estimator and the exponentially weighted aggregate. These bounds highlight the dependence of the estimation error on the size of the data set, the maximum intensity of the interactions, and the level of noise. As the analyzed least-squares estimator is intractable, we propose an adaptation of Lloyd's alternating minimization algorithm to compute an approximation of the least-squares estimator. Finally, we present numerical experiments in order to illustrate the empirical performance of the graphon estimator on synthetic data sets. | ["Xavier D'Haultfoeuille", 'Philippe Choné', 'Francis Kramarz', 'Arnak S. Dalalyan', 'Etienne Donier-Meroz'] | 2023-04-07 | null | null | null | null | ['graphon-estimation'] | ['graphs'] | [-1.05008714e-01 3.32915694e-01 -1.95396647e-01 -3.07203948e-01
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7da36d7d-dffa-4df0-9f6e-54b5291fbd20 | constructing-a-dataset-of-support-and-attack | null | null | https://aclanthology.org/2022.lrec-1.51 | https://aclanthology.org/2022.lrec-1.51.pdf | Constructing A Dataset of Support and Attack Relations in Legal Arguments in Court Judgements using Linguistic Rules | Argumentation mining is a growing area of research and has several interesting practical applications of mining legal arguments. Support and Attack relations are the backbone of any legal argument. However, there is no publicly available dataset of these relations in the context of legal arguments expressed in court judgements. In this paper, we focus on automatically constructing such a dataset of Support and Attack relations between sentences in a court judgment with reasonable accuracy. We propose three sets of rules based on linguistic knowledge and distant supervision to identify such relations from Indian Supreme Court judgments. The first rule set is based on multiple discourse connectors, the second rule set is based on common semantic structures between argumentative sentences in a close neighbourhood, and the third rule set uses the information about the source of the argument. We also explore a BERT-based sentence pair classification model which is trained on this dataset. We release the dataset of 20506 sentence pairs - 10746 Support (precision 77.3%) and 9760 Attack (precision 65.8%). We believe that this dataset and the ideas explored in designing the linguistic rules and will boost the argumentation mining research for legal arguments. | ['Rituraj Singh', 'Girish Palshikar', 'Sachin Pawar', 'Basit Ali'] | null | null | null | null | lrec-2022-6 | ['sentence-pair-classification'] | ['natural-language-processing'] | [ 2.71806121e-01 7.19262779e-01 -4.18617219e-01 -8.17360759e-01
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6387a486-0810-4b75-ba0c-ce7f42cc21e7 | variational-inference-using-implicit | 1702.08235 | null | http://arxiv.org/abs/1702.08235v1 | http://arxiv.org/pdf/1702.08235v1.pdf | Variational Inference using Implicit Distributions | Generative adversarial networks (GANs) have given us a great tool to fit
implicit generative models to data. Implicit distributions are ones we can
sample from easily, and take derivatives of samples with respect to model
parameters. These models are highly expressive and we argue they can prove just
as useful for variational inference (VI) as they are for generative modelling.
Several papers have proposed GAN-like algorithms for inference, however,
connections to the theory of VI are not always well understood. This paper
provides a unifying review of existing algorithms establishing connections
between variational autoencoders, adversarially learned inference, operator VI,
GAN-based image reconstruction, and more. Secondly, the paper provides a
framework for building new algorithms: depending on the way the variational
bound is expressed we introduce prior-contrastive and joint-contrastive
methods, and show practical inference algorithms based on either density ratio
estimation or denoising. | ['Ferenc Huszár'] | 2017-02-27 | null | null | null | null | ['density-ratio-estimation'] | ['methodology'] | [ 1.43258944e-01 4.71654207e-01 -1.46127271e-03 -1.34855926e-01
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4d3a0a30-38e0-429e-92e7-6d8a6641b4d5 | collaborative-transformers-for-grounded | 2203.16518 | null | https://arxiv.org/abs/2203.16518v1 | https://arxiv.org/pdf/2203.16518v1.pdf | Collaborative Transformers for Grounded Situation Recognition | Grounded situation recognition is the task of predicting the main activity, entities playing certain roles within the activity, and bounding-box groundings of the entities in the given image. To effectively deal with this challenging task, we introduce a novel approach where the two processes for activity classification and entity estimation are interactive and complementary. To implement this idea, we propose Collaborative Glance-Gaze TransFormer (CoFormer) that consists of two modules: Glance transformer for activity classification and Gaze transformer for entity estimation. Glance transformer predicts the main activity with the help of Gaze transformer that analyzes entities and their relations, while Gaze transformer estimates the grounded entities by focusing only on the entities relevant to the activity predicted by Glance transformer. Our CoFormer achieves the state of the art in all evaluation metrics on the SWiG dataset. Training code and model weights are available at https://github.com/jhcho99/CoFormer. | ['Suha Kwak', 'Youngseok Yoon', 'Junhyeong Cho'] | 2022-03-30 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Cho_Collaborative_Transformers_for_Grounded_Situation_Recognition_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Cho_Collaborative_Transformers_for_Grounded_Situation_Recognition_CVPR_2022_paper.pdf | cvpr-2022-1 | ['grounded-situation-recognition', 'situation-recognition'] | ['computer-vision', 'computer-vision'] | [ 7.79861510e-02 3.61432314e-01 4.08669598e-02 -2.44923815e-01
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0c662634-c8ec-4f9c-ac04-f7d830ccceef | xbound-former-toward-cross-scale-boundary | 2206.00806 | null | https://arxiv.org/abs/2206.00806v1 | https://arxiv.org/pdf/2206.00806v1.pdf | XBound-Former: Toward Cross-scale Boundary Modeling in Transformers | Skin lesion segmentation from dermoscopy images is of great significance in the quantitative analysis of skin cancers, which is yet challenging even for dermatologists due to the inherent issues, i.e., considerable size, shape and color variation, and ambiguous boundaries. Recent vision transformers have shown promising performance in handling the variation through global context modeling. Still, they have not thoroughly solved the problem of ambiguous boundaries as they ignore the complementary usage of the boundary knowledge and global contexts. In this paper, we propose a novel cross-scale boundary-aware transformer, \textbf{XBound-Former}, to simultaneously address the variation and boundary problems of skin lesion segmentation. XBound-Former is a purely attention-based network and catches boundary knowledge via three specially designed learners. We evaluate the model on two skin lesion datasets, ISIC-2016\&PH$^2$ and ISIC-2018, where our model consistently outperforms other convolution- and transformer-based models, especially on the boundary-wise metrics. We extensively verify the generalization ability of polyp lesion segmentation that has similar characteristics, and our model can also yield significant improvement compared to the latest models. | ['Jing Qin', 'Qichao Zhou', 'Xiangdong Tang', 'Jianwei Shuai', 'Zhaodong Fei', 'Liansheng Wang', 'Yuxi Ma', 'Fei Chen', 'Jiacheng Wang'] | 2022-06-02 | null | null | null | null | ['skin-lesion-segmentation'] | ['medical'] | [ 5.57678640e-01 -1.15508780e-01 -3.08010906e-01 -2.82555521e-01
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85f3a8b3-9749-496b-a219-46cdd35c69b3 | boosting-performance-of-a-baseline-visual | 2210.07509 | null | https://arxiv.org/abs/2210.07509v1 | https://arxiv.org/pdf/2210.07509v1.pdf | Boosting Performance of a Baseline Visual Place Recognition Technique by Predicting the Maximally Complementary Technique | One recent promising approach to the Visual Place Recognition (VPR) problem has been to fuse the place recognition estimates of multiple complementary VPR techniques using methods such as SRAL and multi-process fusion. These approaches come with a substantial practical limitation: they require all potential VPR methods to be brute-force run before they are selectively fused. The obvious solution to this limitation is to predict the viable subset of methods ahead of time, but this is challenging because it requires a predictive signal within the imagery itself that is indicative of high performance methods. Here we propose an alternative approach that instead starts with a known single base VPR technique, and learns to predict the most complementary additional VPR technique to fuse with it, that results in the largest improvement in performance. The key innovation here is to use a dimensionally reduced difference vector between the query image and the top-retrieved reference image using this baseline technique as the predictive signal of the most complementary additional technique, both during training and inference. We demonstrate that our approach can train a single network to select performant, complementary technique pairs across datasets which span multiple modes of transportation (train, car, walking) as well as to generalise to unseen datasets, outperforming multiple baseline strategies for manually selecting the best technique pairs based on the same training data. | ['Michael Milford', 'Tobias Fischer', 'Stephen Hausler', 'Connor Malone'] | 2022-10-14 | null | null | null | null | ['visual-place-recognition'] | ['computer-vision'] | [ 6.03479028e-01 -4.40397680e-01 -2.70221144e-01 -2.24689096e-01
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91556073-fca3-4ac6-982e-3cb2e2b5bb2f | a-machine-learning-approach-for-non-blind | null | null | http://openaccess.thecvf.com/content_cvpr_2013/html/Schuler_A_Machine_Learning_2013_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2013/papers/Schuler_A_Machine_Learning_2013_CVPR_paper.pdf | A Machine Learning Approach for Non-blind Image Deconvolution | Image deconvolution is the ill-posed problem of recovering a sharp image, given a blurry one generated by a convolution. In this work, we deal with space-invariant nonblind deconvolution. Currently, the most successful methods involve a regularized inversion of the blur in Fourier domain as a first step. This step amplifies and colors the noise, and corrupts the image information. In a second (and arguably more difficult) step, one then needs to remove the colored noise, typically using a cleverly engineered algorithm. However, the methods based on this two-step approach do not properly address the fact that the image information has been corrupted. In this work, we also rely on a two-step procedure, but learn the second step on a large dataset of natural images, using a neural network. We will show that this approach outperforms the current state-ofthe-art on a large dataset of artificially blurred images. We demonstrate the practical applicability of our method in a real-world example with photographic out-of-focus blur. | ['Stefan Harmeling', 'Harold Christopher Burger', 'Christian J. Schuler', 'Bernhard Scholkopf'] | 2013-06-01 | null | null | null | cvpr-2013-6 | ['image-deconvolution'] | ['computer-vision'] | [ 6.16063774e-01 -2.42672861e-01 6.62239194e-01 -1.69612706e-01
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bcf60e77-fc61-4efc-a6d7-17af60d54ca0 | advcodec-towards-a-unified-framework-for-1 | 1912.10375 | null | https://arxiv.org/abs/1912.10375v2 | https://arxiv.org/pdf/1912.10375v2.pdf | T3: Tree-Autoencoder Constrained Adversarial Text Generation for Targeted Attack | Adversarial attacks against natural language processing systems, which perform seemingly innocuous modifications to inputs, can induce arbitrary mistakes to the target models. Though raised great concerns, such adversarial attacks can be leveraged to estimate the robustness of NLP models. Compared with the adversarial example generation in continuous data domain (e.g., image), generating adversarial text that preserves the original meaning is challenging since the text space is discrete and non-differentiable. To handle these challenges, we propose a target-controllable adversarial attack framework T3, which is applicable to a range of NLP tasks. In particular, we propose a tree-based autoencoder to embed the discrete text data into a continuous representation space, upon which we optimize the adversarial perturbation. A novel tree-based decoder is then applied to regularize the syntactic correctness of the generated text and manipulate it on either sentence (T3(Sent)) or word (T3(Word)) level. We consider two most representative NLP tasks: sentiment analysis and question answering (QA). Extensive experimental results and human studies show that T3 generated adversarial texts can successfully manipulate the NLP models to output the targeted incorrect answer without misleading the human. Moreover, we show that the generated adversarial texts have high transferability which enables the black-box attacks in practice. Our work sheds light on an effective and general way to examine the robustness of NLP models. Our code is publicly available at https://github.com/AI-secure/T3/. | ['Shuohang Wang', 'Qian Chen', 'Boyuan Pan', 'Bo Li', 'Hengzhi Pei', 'Boxin Wang'] | 2019-12-22 | null | https://aclanthology.org/2020.emnlp-main.495 | https://aclanthology.org/2020.emnlp-main.495.pdf | emnlp-2020-11 | ['adversarial-text'] | ['adversarial'] | [ 6.77977324e-01 4.22194093e-01 3.57164294e-01 -3.06326479e-01
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