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dfbe8b72-b37b-4667-94b3-43eef06ad621 | on-distributed-adaptive-optimization-with-1 | 2205.05632 | null | https://arxiv.org/abs/2205.05632v1 | https://arxiv.org/pdf/2205.05632v1.pdf | On Distributed Adaptive Optimization with Gradient Compression | We study COMP-AMS, a distributed optimization framework based on gradient averaging and adaptive AMSGrad algorithm. Gradient compression with error feedback is applied to reduce the communication cost in the gradient transmission process. Our convergence analysis of COMP-AMS shows that such compressed gradient averaging strategy yields same convergence rate as standard AMSGrad, and also exhibits the linear speedup effect w.r.t. the number of local workers. Compared with recently proposed protocols on distributed adaptive methods, COMP-AMS is simple and convenient. Numerical experiments are conducted to justify the theoretical findings, and demonstrate that the proposed method can achieve same test accuracy as the full-gradient AMSGrad with substantial communication savings. With its simplicity and efficiency, COMP-AMS can serve as a useful distributed training framework for adaptive gradient methods. | ['Ping Li', 'Belhal Karimi', 'Xiaoyun Li'] | 2022-05-11 | on-distributed-adaptive-optimization-with | https://openreview.net/forum?id=CI-xXX9dg9l | https://openreview.net/pdf?id=CI-xXX9dg9l | iclr-2022-4 | ['distributed-optimization'] | ['methodology'] | [-3.65637690e-01 -5.50420463e-01 -1.01820491e-01 -5.34058034e-01
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915cbb06-a8ee-49f2-817f-04bf4eff940e | unexpected-novel-merbecovirus-discoveries-in | 2104.01533 | null | https://arxiv.org/abs/2104.01533v2 | https://arxiv.org/pdf/2104.01533v2.pdf | Unexpected novel Merbecovirus discoveries in agricultural sequencing datasets from Wuhan, China | In this study we document the unexpected discovery of multiple coronaviruses and a BSL-3 pathogen in agricultural cotton and rice sequencing datasets. In particular, we have identified a novel HKU5-related Merbecovirus in a cotton dataset sequenced by the Huazhong Agricultural University in 2017. We have also found an infectious clone sequence containing a novel HKU4-related Merbecovirus related to MERS coronavirus in a rice dataset sequenced by the Huazhong Agricultural University in early 2020. Another HKU5-related Merbecovirus, as well as Japanese encephalitis virus, were identified in a cotton dataset sequenced by the Huazhong Agricultural University in 2018. An HKU3-related Betacoronavirus was found in a Mus musculus sequencing dataset from the Wuhan Institute of Virology in 2017. Finally, a SARS-WIV1-like Betacoronavirus was found in a rice dataset sequenced by the Fujian Agriculture and Forestry University in 2017. Using the contaminating reads we have extracted from the above datasets, we were able to assemble complete genomes of two novel coronaviruses which we disclose herein. In light of our findings, we raise concerns about biosafety protocol breaches, as indicated by our discovery of multiple dangerous human pathogens in agricultural sequencing laboratories in Wuhan and Fouzou City, China. | ['Alejandro Sousa', 'Karl Sirotkin', 'Yuri Deigin', 'Adrian Jones', 'Daoyu Zhang'] | 2021-04-04 | null | null | null | null | ['virology'] | ['miscellaneous'] | [ 5.96096575e-01 -1.64491996e-01 1.53514132e-01 7.51473010e-02
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545d1052-03a1-4b3b-b7e4-da1b0aca3390 | is-pre-training-truly-better-than-meta | 2306.13841 | null | https://arxiv.org/abs/2306.13841v1 | https://arxiv.org/pdf/2306.13841v1.pdf | Is Pre-training Truly Better Than Meta-Learning? | In the context of few-shot learning, it is currently believed that a fixed pre-trained (PT) model, along with fine-tuning the final layer during evaluation, outperforms standard meta-learning algorithms. We re-evaluate these claims under an in-depth empirical examination of an extensive set of formally diverse datasets and compare PT to Model Agnostic Meta-Learning (MAML). Unlike previous work, we emphasize a fair comparison by using: the same architecture, the same optimizer, and all models trained to convergence. Crucially, we use a more rigorous statistical tool -- the effect size (Cohen's d) -- to determine the practical significance of the difference between a model trained with PT vs. a MAML. We then use a previously proposed metric -- the diversity coefficient -- to compute the average formal diversity of a dataset. Using this analysis, we demonstrate the following: 1. when the formal diversity of a data set is low, PT beats MAML on average and 2. when the formal diversity is high, MAML beats PT on average. The caveat is that the magnitude of the average difference between a PT vs. MAML using the effect size is low (according to classical statistical thresholds) -- less than 0.2. Nevertheless, this observation is contrary to the currently held belief that a pre-trained model is always better than a meta-learning model. Our extensive experiments consider 21 few-shot learning benchmarks, including the large-scale few-shot learning dataset Meta-Data set. We also show no significant difference between a MAML model vs. a PT model with GPT-2 on Openwebtext. We, therefore, conclude that a pre-trained model does not always beat a meta-learned model and that the formal diversity of a dataset is a driving factor. | ['Sanmi Koyejo', 'Yu-Xiong Wang', 'Saumya Goyal', 'Patrick Yu', 'Brando Miranda'] | 2023-06-24 | null | null | null | null | ['meta-learning', 'few-shot-learning'] | ['methodology', 'methodology'] | [ 1.99674368e-01 6.42004833e-02 -3.37039262e-01 -1.30552694e-01
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1a1c10b7-c4ac-4298-a190-916f07c8d35d | connecting-surrogate-safety-measures-to-crash | 2210.01363 | null | https://arxiv.org/abs/2210.01363v1 | https://arxiv.org/pdf/2210.01363v1.pdf | Connecting Surrogate Safety Measures to Crash Probablity via Causal Probabilistic Time Series Prediction | Surrogate safety measures can provide fast and pro-active safety analysis and give insights on the pre-crash process and crash failure mechanism by studying near misses. However, validating surrogate safety measures by connecting them to crashes is still an open question. This paper proposed a method to connect surrogate safety measures to crash probability using probabilistic time series prediction. The method used sequences of speed, acceleration and time-to-collision to estimate the probability density functions of those variables with transformer masked autoregressive flow (transformer-MAF). The autoregressive structure mimicked the causal relationship between condition, action and crash outcome and the probability density functions are used to calculate the conditional action probability, crash probability and conditional crash probability. The predicted sequence is accurate and the estimated probability is reasonable under both traffic conflict context and normal interaction context and the conditional crash probability shows the effectiveness of evasive action to avoid crashes in a counterfactual experiment. | ['Mark Hansen', 'Offer Grembek', 'Jiajian Lu'] | 2022-10-04 | null | null | null | null | ['time-series-prediction'] | ['time-series'] | [-1.54095471e-01 -3.76841515e-01 -2.05585167e-01 -3.59233886e-01
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761029be-ac72-4fe4-aa5e-83dfd5226155 | prototypical-verbalizer-for-prompt-based-few-1 | 2203.09770 | null | https://arxiv.org/abs/2203.09770v1 | https://arxiv.org/pdf/2203.09770v1.pdf | Prototypical Verbalizer for Prompt-based Few-shot Tuning | Prompt-based tuning for pre-trained language models (PLMs) has shown its effectiveness in few-shot learning. Typically, prompt-based tuning wraps the input text into a cloze question. To make predictions, the model maps the output words to labels via a verbalizer, which is either manually designed or automatically built. However, manual verbalizers heavily depend on domain-specific prior knowledge and human efforts, while finding appropriate label words automatically still remains challenging.In this work, we propose the prototypical verbalizer (ProtoVerb) which is built directly from training data. Specifically, ProtoVerb learns prototype vectors as verbalizers by contrastive learning. In this way, the prototypes summarize training instances and are able to enclose rich class-level semantics. We conduct experiments on both topic classification and entity typing tasks, and the results demonstrate that ProtoVerb significantly outperforms current automatic verbalizers, especially when training data is extremely scarce. More surprisingly, ProtoVerb consistently boosts prompt-based tuning even on untuned PLMs, indicating an elegant non-tuning way to utilize PLMs. Our codes are avaliable at https://github.com/thunlp/OpenPrompt. | ['Zhiyuan Liu', 'Longtao Huang', 'Ning Ding', 'Shengding Hu', 'Ganqu Cui'] | 2022-03-18 | null | https://aclanthology.org/2022.acl-long.483 | https://aclanthology.org/2022.acl-long.483.pdf | acl-2022-5 | ['entity-typing'] | ['natural-language-processing'] | [-9.54468325e-02 1.88441023e-01 -6.33905113e-01 -4.72720981e-01
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1c15322b-f4b1-4232-b849-d5dcd6d8da5c | self-learning-eigenstates-with-a-quantum | 2006.13222 | null | https://arxiv.org/abs/2006.13222v2 | https://arxiv.org/pdf/2006.13222v2.pdf | Certified variational quantum algorithms for eigenstate preparation | Solutions to many-body problem instances often involve an intractable number of degrees of freedom and admit no known approximations in general form. In practice, representing quantum-mechanical states of a given Hamiltonian using available numerical methods, in particular those based on variational Monte Carlo simulations, become exponentially more challenging with increasing system size. Recently quantum algorithms implemented as variational models have been proposed to accelerate such simulations. The variational ansatz states are characterized by a polynomial number of parameters devised in a way to minimize the expectation value of a given Hamiltonian, which is emulated by local measurements. In this study, we develop a means to certify the termination of variational algorithms. We demonstrate our approach by applying it to three models: the transverse field Ising model, the model of one-dimensional spinless fermions with competing interactions, and the Schwinger model of quantum electrodynamics. By means of comparison, we observe that our approach shows better performance near critical points in these models. We hence take a further step to improve the applicability and to certify the results of variational quantum simulators. | ['Dmitry Yudin', 'Jacob Biamonte', 'Andrey Kardashin', 'Alexey Uvarov'] | 2020-06-23 | null | null | null | null | ['variational-monte-carlo'] | ['miscellaneous'] | [ 1.69613034e-01 -1.82444960e-01 2.90790141e-01 -1.40773073e-01
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0289ec98-956f-4a1b-a02b-6c2b3640384d | from-linguistic-resources-to-ontology-aware | null | null | https://aclanthology.org/2020.lrec-1.305 | https://aclanthology.org/2020.lrec-1.305.pdf | From Linguistic Resources to Ontology-Aware Terminologies: Minding the Representation Gap | Terminological resources have proven crucial in many applications ranging from Computer-Aided Translation tools to authoring softwares and multilingual and cross-lingual information retrieval systems. Nonetheless, with the exception of a few felicitous examples, such as the IATE (Interactive Terminology for Europe) Termbank, many terminological resources are not available in standard formats, such as Term Base eXchange (TBX), thus preventing their sharing and reuse. Yet, these terminologies could be improved associating the correspondent ontology-based information. The research described in the present contribution demonstrates the process and the methodologies adopted in the automatic conversion into TBX of such type of resources, together with their semantic enrichment based on the formalization of ontological information into terminologies. We present a proof-of-concept using the Italian Linguistic Resource for the Archaeological domain (developed according to Thesauri and Guidelines of the Italian Central Institute for the Catalogue and Documentation). Further, we introduce the conversion tool developed to support the process of creating ontology-aware terminologies for improving interoperability and sharing of existing language technologies and data sets. | ['Federico Sangati', 'Giulia Speranza', 'Maria Pia di Buono', 'Johanna Monti'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['cross-lingual-information-retrieval'] | ['natural-language-processing'] | [-9.55601782e-02 3.06528091e-01 -2.28860423e-01 -2.41392348e-02
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a6cf0532-1b8a-43e3-a11d-a5e7020f1509 | class-agnostic-few-shot-object-counting | null | null | https://ieeexplore.ieee.org/document/9423155 | https://openaccess.thecvf.com/content/WACV2021/papers/Yang_Class-Agnostic_Few-Shot_Object_Counting_WACV_2021_paper.pdf | Class-agnostic-Few-shot-Object-Counting | Object counting which aims to calculate the number of total instances of the given class is a classic but crucial task that can be applied to many applications. Most of the prior works only focus on counting certain classes of objects such as people, cars, animals, etc. However, in recent years, there are lots of applications that need to get the count of the unseen class of objects such as a mechanical arm commanded to grab the novel object. In this paper, we present an effective object counting network, Class-agnostic Few-shot Object Counting Network (CFOCNet), that supports counting arbitrary classes of object unseen during training stage. Instead of counting a pre-defined class, our model is able to count instances based on input reference images and reduces the huge cost of data collection, training and parameter tuning for each new object class. Our model utilizes not only the similarity between query image and reference images but self attending the query image to learn the self-repeatedness. Using a two-stream Resnet that matches features in different scales, our network can automatically learn to aggregate different scales of the matching scores. We evaluate our method on the subset of the COCO dataset that contains 80 classes of objects and many diverse scenes. In the experiments, our network outperforms other methods including detection and some previous works by a large margin. To the best of our knowledge, we are the first that mainly focuses on few-shot object counting in the class-agnostic manner. | ['Wen-Chin Chen', 'Winston H. Hsu', 'Hung-Ting Su', 'Shuo-Diao Yang'] | 2021-06-14 | null | null | null | wacv-2021-6 | ['object-counting'] | ['computer-vision'] | [ 2.17286304e-01 -7.75399804e-01 6.47258312e-02 -4.11645353e-01
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bb8131eb-b2d3-4549-96b3-0e98dee881f8 | treasure-in-distribution-a-domain | 2305.19949 | null | https://arxiv.org/abs/2305.19949v1 | https://arxiv.org/pdf/2305.19949v1.pdf | Treasure in Distribution: A Domain Randomization based Multi-Source Domain Generalization for 2D Medical Image Segmentation | Although recent years have witnessed the great success of convolutional neural networks (CNNs) in medical image segmentation, the domain shift issue caused by the highly variable image quality of medical images hinders the deployment of CNNs in real-world clinical applications. Domain generalization (DG) methods aim to address this issue by training a robust model on the source domain, which has a strong generalization ability. Previously, many DG methods based on feature-space domain randomization have been proposed, which, however, suffer from the limited and unordered search space of feature styles. In this paper, we propose a multi-source DG method called Treasure in Distribution (TriD), which constructs an unprecedented search space to obtain the model with strong robustness by randomly sampling from a uniform distribution. To learn the domain-invariant representations explicitly, we further devise a style-mixing strategy in our TriD, which mixes the feature styles by randomly mixing the augmented and original statistics along the channel wise and can be extended to other DG methods. Extensive experiments on two medical segmentation tasks with different modalities demonstrate that our TriD achieves superior generalization performance on unseen target-domain data. Code is available at https://github.com/Chen-Ziyang/TriD. | ['Yong Xia', 'Hengfei Cui', 'Yiwen Ye', 'Yongsheng Pan', 'Ziyang Chen'] | 2023-05-31 | null | null | null | null | ['domain-generalization'] | ['methodology'] | [ 4.15291339e-01 -3.08911949e-01 -3.55974078e-01 -4.17371482e-01
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361c08fa-84c9-4980-8035-eae922353e58 | dynaquant-compressing-deep-learning-training | 2306.11800 | null | https://arxiv.org/abs/2306.11800v1 | https://arxiv.org/pdf/2306.11800v1.pdf | DynaQuant: Compressing Deep Learning Training Checkpoints via Dynamic Quantization | With the increase in the scale of Deep Learning (DL) training workloads in terms of compute resources and time consumption, the likelihood of encountering in-training failures rises substantially, leading to lost work and resource wastage. Such failures are typically offset by a checkpointing mechanism, which comes at the cost of storage and network bandwidth overhead. State-of-the-art approaches involve lossy model compression mechanisms, which induce a tradeoff between the resulting model quality (accuracy) and compression ratio. Delta compression is then also used to further reduce the overhead by only storing the difference between consecutive checkpoints. We make a key enabling observation that the sensitivity of model weights to compression varies during training, and different weights benefit from different quantization levels (ranging from retaining full precision to pruning). We propose (1) a non-uniform quantization scheme that leverages this variation, (2) an efficient search mechanism to dynamically adjust to the best quantization configurations, and (3) a quantization-aware delta compression mechanism that rearranges weights to minimize checkpoint differences, thereby maximizing compression. We instantiate these contributions in DynaQuant - a framework for DL workload checkpoint compression. Our experiments show that DynaQuant consistently achieves better tradeoff between accuracy and compression ratios compared to prior works, enabling a compression ratio up to 39x and withstanding up to 10 restores with negligible accuracy impact for fault-tolerant training. DynaQuant achieves at least an order of magnitude reduction in checkpoint storage overhead for training failure recovery as well as transfer learning use cases without any loss of accuracy | ['Alexey Tumanov', 'Kexin Rong', 'Vidushi Vashishth', 'Venkata Prabhakara Sarath Nookala', 'Satwik Bhattamishra', 'Sameer Reddy', 'Amey Agrawal'] | 2023-06-20 | null | null | null | null | ['quantization', 'model-compression', 'transfer-learning'] | ['methodology', 'methodology', 'miscellaneous'] | [ 4.91579957e-02 -2.52066433e-01 -6.44805849e-01 -3.28894347e-01
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938c1e37-cb36-4d37-9aa4-0dc51a1d82d8 | how-to-train-unstable-looped-tensor-network | 2203.02617 | null | https://arxiv.org/abs/2203.02617v1 | https://arxiv.org/pdf/2203.02617v1.pdf | How to Train Unstable Looped Tensor Network | A rising problem in the compression of Deep Neural Networks is how to reduce the number of parameters in convolutional kernels and the complexity of these layers by low-rank tensor approximation. Canonical polyadic tensor decomposition (CPD) and Tucker tensor decomposition (TKD) are two solutions to this problem and provide promising results. However, CPD often fails due to degeneracy, making the networks unstable and hard to fine-tune. TKD does not provide much compression if the core tensor is big. This motivates using a hybrid model of CPD and TKD, a decomposition with multiple Tucker models with small core tensor, known as block term decomposition (BTD). This paper proposes a more compact model that further compresses the BTD by enforcing core tensors in BTD identical. We establish a link between the BTD with shared parameters and a looped chain tensor network (TC). Unfortunately, such strongly constrained tensor networks (with loop) encounter severe numerical instability, as proved by y (Landsberg, 2012) and (Handschuh, 2015a). We study perturbation of chain tensor networks, provide interpretation of instability in TC, demonstrate the problem. We propose novel methods to gain the stability of the decomposition results, keep the network robust and attain better approximation. Experimental results will confirm the superiority of the proposed methods in compression of well-known CNNs, and TC decomposition under challenging scenarios | ['Andrzej Cichocki', 'Petr Tichavsky', 'Nikolay Kozyrskiy', 'Igor Vorona', 'Dmitry Ermilov', 'Konstantin Sobolev', 'Anh-Huy Phan'] | 2022-03-05 | null | null | null | null | ['tensor-networks'] | ['methodology'] | [-2.34269840e-03 -7.52077103e-02 -2.04488505e-02 1.07461385e-01
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b13cb5df-5ddc-46e1-8041-0c68644f157c | a-structured-learning-approach-to-temporal-1 | 1906.04943 | null | https://arxiv.org/abs/1906.04943v1 | https://arxiv.org/pdf/1906.04943v1.pdf | A Structured Learning Approach to Temporal Relation Extraction | Identifying temporal relations between events is an essential step towards natural language understanding. However, the temporal relation between two events in a story depends on, and is often dictated by, relations among other events. Consequently, effectively identifying temporal relations between events is a challenging problem even for human annotators. This paper suggests that it is important to take these dependencies into account while learning to identify these relations and proposes a structured learning approach to address this challenge. As a byproduct, this provides a new perspective on handling missing relations, a known issue that hurts existing methods. As we show, the proposed approach results in significant improvements on the two commonly used data sets for this problem. | ['Zhili Feng', 'Qiang Ning', 'Dan Roth'] | 2019-06-12 | a-structured-learning-approach-to-temporal | https://aclanthology.org/D17-1108 | https://aclanthology.org/D17-1108.pdf | emnlp-2017-9 | ['temporal-relation-extraction'] | ['natural-language-processing'] | [ 2.06373751e-01 6.89484999e-02 -5.04905105e-01 -5.11168957e-01
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b6cacd5f-4ffe-44ee-ac9f-676c3cd63f76 | extracting-covid-19-events-from-twitter | 2006.02567 | null | https://arxiv.org/abs/2006.02567v4 | https://arxiv.org/pdf/2006.02567v4.pdf | Extracting a Knowledge Base of COVID-19 Events from Social Media | In this paper, we present a manually annotated corpus of 10,000 tweets containing public reports of five COVID-19 events, including positive and negative tests, deaths, denied access to testing, claimed cures and preventions. We designed slot-filling questions for each event type and annotated a total of 31 fine-grained slots, such as the location of events, recent travel, and close contacts. We show that our corpus can support fine-tuning BERT-based classifiers to automatically extract publicly reported events and help track the spread of a new disease. We also demonstrate that, by aggregating events extracted from millions of tweets, we achieve surprisingly high precision when answering complex queries, such as "Which organizations have employees that tested positive in Philadelphia?" We will release our corpus (with user-information removed), automatic extraction models, and the corresponding knowledge base to the research community. | ['Wei Xu', 'Alan Ritter', 'Shi Zong', 'Ashutosh Baheti'] | 2020-06-03 | null | https://aclanthology.org/2022.coling-1.335 | https://aclanthology.org/2022.coling-1.335.pdf | coling-2022-10 | ['extracting-covid-19-events-from-twitter'] | ['natural-language-processing'] | [ 1.36871278e-01 3.49926949e-01 -6.25740886e-01 -3.95948946e-01
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cacd9601-2fb2-42f9-b6dd-82d18d6bf8c7 | a-novel-uncertainty-aware-collaborative | 2105.05496 | null | https://arxiv.org/abs/2105.05496v2 | https://arxiv.org/pdf/2105.05496v2.pdf | A Consensual Collaborative Learning Method for Remote Sensing Image Classification Under Noisy Multi-Labels | Collecting a large number of reliable training images annotated by multiple land-cover class labels in the framework of multi-label classification is time-consuming and costly in remote sensing (RS). To address this problem, publicly available thematic products are often used for annotating RS images with zero-labeling-cost. However, such an approach may result in constructing a training set with noisy multi-labels, distorting the learning process. To address this problem, we propose a Consensual Collaborative Multi-Label Learning (CCML) method. The proposed CCML identifies, ranks and corrects training images with noisy multi-labels through four main modules: 1) discrepancy module; 2) group lasso module; 3) flipping module; and 4) swap module. The discrepancy module ensures that the two networks learn diverse features, while obtaining the same predictions. The group lasso module detects the potentially noisy labels by estimating the label uncertainty based on the aggregation of two collaborative networks. The flipping module corrects the identified noisy labels, whereas the swap module exchanges the ranking information between the two networks. The experimental results confirm the success of the proposed CCML under high (synthetically added) multi-label noise rates. The code of the proposed method is publicly available at https://noisy-labels-in-rs.org | ['Begum Demir', 'Tristan Kreuziger', 'Mahdyar Ravanbakhsh', 'Ahmet Kerem Aksoy'] | 2021-05-12 | null | null | null | null | ['remote-sensing-image-classification'] | ['miscellaneous'] | [ 4.39814895e-01 6.31069094e-02 -5.22317598e-03 -6.20763302e-01
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e9d8a86c-5494-4aab-831d-7b96ce7b4d56 | extractive-multi-document-summarization-using-2 | null | null | https://www.researchgate.net/publication/338101046_Extractive_Multi-document_Summarization_using_K-means_Centroid-based_Method_MMR_and_Sentence_Position | https://www.researchgate.net/publication/338101046_Extractive_Multi-document_Summarization_using_K-means_Centroid-based_Method_MMR_and_Sentence_Position | Extractive Multi-document Summarization using K-means, Centroid-based Method, MMR, and Sentence Position | Multi-document summarization is more challenging than single-document summarization since it has to solve the problem of overlapping information among sentences from different documents. Also, since multi-document summarization dataset is rare, methods based on deep learning are difficult to be applied. In this paper, we propose an approach to multi-document summarization based on a k-means clustering algorithm, combining with the centroid-based method, maximal marginal relevance, and sentence positions. This approach is efficient in finding salient sentences and preventing overlapping between sentences. Experiments using DUC 2007 dataset show that our system is more efficient than other researchers in this field. | ['Tuan Luu Minh', 'Huong Le Thanh', 'Hai Cao Manh'] | 2019-12-04 | null | null | null | the-tenth-international-symposium-2019-12 | ['extractive-document-summarization'] | ['natural-language-processing'] | [ 2.10361451e-01 -1.54196337e-01 -2.48454824e-01 -4.34793942e-02
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0f515cb0-53ec-4ce5-9a90-9f7a613f0425 | ttui-at-semeval-2020-task-11-propaganda | null | null | https://aclanthology.org/2020.semeval-1.240 | https://aclanthology.org/2020.semeval-1.240.pdf | TTUI at SemEval-2020 Task 11: Propaganda Detection with Transfer Learning and Ensembles | In this paper, we describe our approaches and systems for the SemEval-2020 Task 11 on propaganda technique detection. We fine-tuned BERT and RoBERTa pre-trained models then merged them with an average ensemble. We conducted several experiments for input representations dealing with long texts and preserving context as well as for the imbalanced class problem. Our system ranked 20th out of 36 teams with 0.398 F1 in the SI task and 14th out of 31 teams with 0.556 F1 in the TC task. | ['Steven Bethard', 'Moonsung Kim'] | 2020-12-01 | null | null | null | semeval-2020 | ['propaganda-detection'] | ['natural-language-processing'] | [-1.18420333e-01 7.26449415e-02 -4.63523924e-01 -1.44542232e-01
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16f597fc-2003-47f4-a6da-8dd218baf23a | diva-hisdb-a-precisely-annotated-large | null | null | https://diuf.unifr.ch/main/hisdoc/diva-hisdb | https://diuf.unifr.ch/main/hisdoc/sites/diuf.unifr.ch.main.hisdoc/files/uploads/hisdoc2.0-publications/2016-icfhr-divahisdb.pdf | DIVA-HisDB: A Precisely Annotated Large Dataset of Challenging Medieval Manuscripts | This paper introduces a publicly available historical manuscript database DIVA-HisDB for the evaluation of several Document Image Analysis (DIA) tasks. The database consists of 150 annotated pages of three different medieval manuscripts with challenging layouts. Furthermore, we provide a layout analysis ground-truth which has been iterated on, reviewed, and refined by an expert in medieval studies. DIVA-HisDB and the ground truth can be used for training and evaluating DIA tasks, such as layout analysis, text line segmentation, binarization and writer identification. Layout analysis results of several representative baseline technologies are also presented in order to help researchers evaluate their methods and advance the frontiers of complex historical manuscripts analysis. An optimized state-of-the-art Convolutional AutoEncoder (CAE) performs with around 95 % accuracy, demonstrating that for this challenging layout there is much room for improvement. Finally, we show that existing text line segmentation methods fail due to interlinear and marginal text elements. | ['Rolf Ingold', 'Marcus Liwicki', 'Angelika Garz', 'Nicole Eichenberger', 'Mathias Seuret', 'Fotini Simistira'] | 2016-10-23 | null | null | null | international-conference-on-frontiers-in | ['document-layout-analysis', 'text-line-extraction'] | ['computer-vision', 'computer-vision'] | [-1.47145823e-01 -3.68775338e-01 2.56347477e-01 -2.02211678e-01
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46d4b54b-9916-44e0-839e-9165328ed426 | consistent-teacher-provides-better-1 | 2209.01589 | null | https://arxiv.org/abs/2209.01589v3 | https://arxiv.org/pdf/2209.01589v3.pdf | Consistent-Teacher: Towards Reducing Inconsistent Pseudo-targets in Semi-supervised Object Detection | In this study, we dive deep into the inconsistency of pseudo targets in semi-supervised object detection (SSOD). Our core observation is that the oscillating pseudo-targets undermine the training of an accurate detector. It injects noise into the student's training, leading to severe overfitting problems. Therefore, we propose a systematic solution, termed ConsistentTeacher, to reduce the inconsistency. First, adaptive anchor assignment~(ASA) substitutes the static IoU-based strategy, which enables the student network to be resistant to noisy pseudo-bounding boxes. Then we calibrate the subtask predictions by designing a 3D feature alignment module~(FAM-3D). It allows each classification feature to adaptively query the optimal feature vector for the regression task at arbitrary scales and locations. Lastly, a Gaussian Mixture Model (GMM) dynamically revises the score threshold of pseudo-bboxes, which stabilizes the number of ground truths at an early stage and remedies the unreliable supervision signal during training. ConsistentTeacher provides strong results on a large range of SSOD evaluations. It achieves 40.0 mAP with ResNet-50 backbone given only 10% of annotated MS-COCO data, which surpasses previous baselines using pseudo labels by around 3 mAP. When trained on fully annotated MS-COCO with additional unlabeled data, the performance further increases to 47.7 mAP. Our code is available at \url{https://github.com/Adamdad/ConsistentTeacher}. | ['Wayne Zhang', 'Kai Chen', 'Chengqi Lyu', 'Shijie Fang', 'Litong Feng', 'Yijiang Li', 'Shilong Zhang', 'Xingyi Yang', 'Xinjiang Wang'] | 2022-09-04 | consistent-teacher-provides-better | http://openaccess.thecvf.com//content/CVPR2023/html/Wang_Consistent-Teacher_Towards_Reducing_Inconsistent_Pseudo-Targets_in_Semi-Supervised_Object_Detection_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wang_Consistent-Teacher_Towards_Reducing_Inconsistent_Pseudo-Targets_in_Semi-Supervised_Object_Detection_CVPR_2023_paper.pdf | cvpr-2023-1 | ['semi-supervised-object-detection'] | ['computer-vision'] | [-1.01674728e-01 2.23094761e-01 -3.24394166e-01 -4.96636063e-01
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fdb586aa-dead-4a87-831f-3605cdc7b646 | asper-answer-set-programming-enhanced-neural | 2305.15374 | null | https://arxiv.org/abs/2305.15374v1 | https://arxiv.org/pdf/2305.15374v1.pdf | ASPER: Answer Set Programming Enhanced Neural Network Models for Joint Entity-Relation Extraction | A plethora of approaches have been proposed for joint entity-relation (ER) extraction. Most of these methods largely depend on a large amount of manually annotated training data. However, manual data annotation is time consuming, labor intensive, and error prone. Human beings learn using both data (through induction) and knowledge (through deduction). Answer Set Programming (ASP) has been a widely utilized approach for knowledge representation and reasoning that is elaboration tolerant and adept at reasoning with incomplete information. This paper proposes a new approach, ASP-enhanced Entity-Relation extraction (ASPER), to jointly recognize entities and relations by learning from both data and domain knowledge. In particular, ASPER takes advantage of the factual knowledge (represented as facts in ASP) and derived knowledge (represented as rules in ASP) in the learning process of neural network models. We have conducted experiments on two real datasets and compare our method with three baselines. The results show that our ASPER model consistently outperforms the baselines. | ['Tran Cao Son', 'Huiping Cao', 'Trung Hoang Le'] | 2023-05-24 | null | null | null | null | ['relation-extraction'] | ['natural-language-processing'] | [ 6.25412539e-02 6.92632616e-01 -5.24195671e-01 -6.25000596e-01
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9f8dc02f-c33c-43b6-a8d9-99e8947ee11c | identifiability-guaranteed-simplex-structured | 2106.09070 | null | https://arxiv.org/abs/2106.09070v1 | https://arxiv.org/pdf/2106.09070v1.pdf | Identifiability-Guaranteed Simplex-Structured Post-Nonlinear Mixture Learning via Autoencoder | This work focuses on the problem of unraveling nonlinearly mixed latent components in an unsupervised manner. The latent components are assumed to reside in the probability simplex, and are transformed by an unknown post-nonlinear mixing system. This problem finds various applications in signal and data analytics, e.g., nonlinear hyperspectral unmixing, image embedding, and nonlinear clustering. Linear mixture learning problems are already ill-posed, as identifiability of the target latent components is hard to establish in general. With unknown nonlinearity involved, the problem is even more challenging. Prior work offered a function equation-based formulation for provable latent component identification. However, the identifiability conditions are somewhat stringent and unrealistic. In addition, the identifiability analysis is based on the infinite sample (i.e., population) case, while the understanding for practical finite sample cases has been elusive. Moreover, the algorithm in the prior work trades model expressiveness with computational convenience, which often hinders the learning performance. Our contribution is threefold. First, new identifiability conditions are derived under largely relaxed assumptions. Second, comprehensive sample complexity results are presented -- which are the first of the kind. Third, a constrained autoencoder-based algorithmic framework is proposed for implementation, which effectively circumvents the challenges in the existing algorithm. Synthetic and real experiments corroborate our theoretical analyses. | ['Xiao Fu', 'Qi Lyu'] | 2021-06-16 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 4.75529343e-01 1.54663678e-02 -3.12513977e-01 1.18060745e-01
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b2cae581-cd50-4de8-b87c-6428f81ce0ec | single-source-one-shot-reenactment-using | 2104.03117 | null | https://arxiv.org/abs/2104.03117v1 | https://arxiv.org/pdf/2104.03117v1.pdf | Single Source One Shot Reenactment using Weighted motion From Paired Feature Points | Image reenactment is a task where the target object in the source image imitates the motion represented in the driving image. One of the most common reenactment tasks is face image animation. The major challenge in the current face reenactment approaches is to distinguish between facial motion and identity. For this reason, the previous models struggle to produce high-quality animations if the driving and source identities are different (cross-person reenactment). We propose a new (face) reenactment model that learns shape-independent motion features in a self-supervised setup. The motion is represented using a set of paired feature points extracted from the source and driving images simultaneously. The model is generalised to multiple reenactment tasks including faces and non-face objects using only a single source image. The extensive experiments show that the model faithfully transfers the driving motion to the source while retaining the source identity intact. | ['Esa Rahtu', 'Juho Kannala', 'Soumya Tripathy'] | 2021-04-07 | null | null | null | null | ['face-reenactment', 'image-animation'] | ['computer-vision', 'computer-vision'] | [ 2.70877779e-01 1.49305180e-01 -1.78783849e-01 -3.82406622e-01
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4636224e-70b9-4e3d-970f-9c5f80cfcf1b | stoa-vlp-spatial-temporal-modeling-of-object | 2302.09736 | null | https://arxiv.org/abs/2302.09736v2 | https://arxiv.org/pdf/2302.09736v2.pdf | STOA-VLP: Spatial-Temporal Modeling of Object and Action for Video-Language Pre-training | Although large-scale video-language pre-training models, which usually build a global alignment between the video and the text, have achieved remarkable progress on various downstream tasks, the idea of adopting fine-grained information during the pre-training stage is not well explored. In this work, we propose STOA-VLP, a pre-training framework that jointly models object and action information across spatial and temporal dimensions. More specifically, the model regards object trajectories across frames and multiple action features from the video as fine-grained features. Besides, We design two auxiliary tasks to better incorporate both kinds of information into the pre-training process of the video-language model. The first is the dynamic object-text alignment task, which builds a better connection between object trajectories and the relevant noun tokens. The second is the spatial-temporal action set prediction, which guides the model to generate consistent action features by predicting actions found in the text. Extensive experiments on three downstream tasks (video captioning, text-video retrieval, and video question answering) demonstrate the effectiveness of our proposed STOA-VLP (e.g. 3.7 Rouge-L improvements on MSR-VTT video captioning benchmark, 2.9% accuracy improvements on MSVD video question answering benchmark, compared to previous approaches). | ['Bing Qin', 'Xiaocheng Feng', 'Heng Gong', 'Xuan Luo', 'Duyu Tang', 'Mao Zheng', 'Weihong Zhong'] | 2023-02-20 | null | null | null | null | ['video-question-answering', 'video-retrieval'] | ['computer-vision', 'computer-vision'] | [ 2.53465742e-01 -3.55308354e-01 -4.64509070e-01 -3.99377048e-01
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26c34dc1-8381-4c9f-8f30-15886a7455b2 | improved-variational-neural-machine | 1909.09237 | null | https://arxiv.org/abs/1909.09237v1 | https://arxiv.org/pdf/1909.09237v1.pdf | Improved Variational Neural Machine Translation by Promoting Mutual Information | Posterior collapse plagues VAEs for text, especially for conditional text generation with strong autoregressive decoders. In this work, we address this problem in variational neural machine translation by explicitly promoting mutual information between the latent variables and the data. Our model extends the conditional variational autoencoder (CVAE) with two new ingredients: first, we propose a modified evidence lower bound (ELBO) objective which explicitly promotes mutual information; second, we regularize the probabilities of the decoder by mixing an auxiliary factorized distribution which is directly predicted by the latent variables. We present empirical results on the Transformer architecture and show the proposed model effectively addressed posterior collapse: latent variables are no longer ignored in the presence of powerful decoder. As a result, the proposed model yields improved translation quality while demonstrating superior performance in terms of data efficiency and robustness. | ['Xi-An Li', 'Jiatao Gu', 'Arya D. McCarthy', 'Ning Dong'] | 2019-09-19 | null | null | null | null | ['conditional-text-generation'] | ['natural-language-processing'] | [ 9.80979353e-02 3.88266504e-01 -2.45888799e-01 -8.32309574e-03
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6efcbd90-9675-4cab-8668-b3de8af54d9d | a-lightweight-graph-transformer-network-for | 2111.12696 | null | https://arxiv.org/abs/2111.12696v3 | https://arxiv.org/pdf/2111.12696v3.pdf | A Lightweight Graph Transformer Network for Human Mesh Reconstruction from 2D Human Pose | Existing deep learning-based human mesh reconstruction approaches have a tendency to build larger networks in order to achieve higher accuracy. Computational complexity and model size are often neglected, despite being key characteristics for practical use of human mesh reconstruction models (e.g. virtual try-on systems). In this paper, we present GTRS, a lightweight pose-based method that can reconstruct human mesh from 2D human pose. We propose a pose analysis module that uses graph transformers to exploit structured and implicit joint correlations, and a mesh regression module that combines the extracted pose feature with the mesh template to reconstruct the final human mesh. We demonstrate the efficiency and generalization of GTRS by extensive evaluations on the Human3.6M and 3DPW datasets. In particular, GTRS achieves better accuracy than the SOTA pose-based method Pose2Mesh while only using 10.2% of the parameters (Params) and 2.5% of the FLOPs on the challenging in-the-wild 3DPW dataset. Code will be publicly available. | ['Chen Chen', 'Aidong Lu', 'Pu Wang', 'Matias Mendieta', 'Ce Zheng'] | 2021-11-24 | null | null | null | null | ['human-mesh-recovery'] | ['computer-vision'] | [-3.59494597e-01 4.37998533e-01 1.77413791e-01 -2.90434033e-01
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fc63a5f6-2d23-41f2-bb18-e1ec261d8af0 | deep-learning-applications-for-lung-cancer | 2201.00227 | null | https://arxiv.org/abs/2201.00227v1 | https://arxiv.org/pdf/2201.00227v1.pdf | Deep Learning Applications for Lung Cancer Diagnosis: A systematic review | Lung cancer has been one of the most prevalent disease in recent years. According to the research of this field, more than 200,000 cases are identified each year in the US. Uncontrolled multiplication and growth of the lung cells result in malignant tumour formation. Recently, deep learning algorithms, especially Convolutional Neural Networks (CNN), have become a superior way to automatically diagnose disease. The purpose of this article is to review different models that lead to different accuracy and sensitivity in the diagnosis of early-stage lung cancer and to help physicians and researchers in this field. The main purpose of this work is to identify the challenges that exist in lung cancer based on deep learning. The survey is systematically written that combines regular mapping and literature review to review 32 conference and journal articles in the field from 2016 to 2021. After analysing and reviewing the articles, the questions raised in the articles are being answered. This research is superior to other review articles in this field due to the complete review of relevant articles and systematic write up. | ['Shabnam Shadroo', 'Reza Monsefi', 'Hesamoddin Hosseini'] | 2022-01-01 | null | null | null | null | ['lung-cancer-diagnosis'] | ['medical'] | [-1.57128707e-01 4.99226935e-02 -7.70839453e-01 5.09992778e-01
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ae0432eb-64a2-43aa-ac5b-7bf83b9709b4 | a-clustering-guided-contrastive-fusion-for | 2212.13726 | null | https://arxiv.org/abs/2212.13726v2 | https://arxiv.org/pdf/2212.13726v2.pdf | A Clustering-guided Contrastive Fusion for Multi-view Representation Learning | The past two decades have seen increasingly rapid advances in the field of multi-view representation learning due to it extracting useful information from diverse domains to facilitate the development of multi-view applications. However, the community faces two challenges: i) how to learn robust representations from a large amount of unlabeled data to against noise or incomplete views setting, and ii) how to balance view consistency and complementary for various downstream tasks. To this end, we utilize a deep fusion network to fuse view-specific representations into the view-common representation, extracting high-level semantics for obtaining robust representation. In addition, we employ a clustering task to guide the fusion network to prevent it from leading to trivial solutions. For balancing consistency and complementary, then, we design an asymmetrical contrastive strategy that aligns the view-common representation and each view-specific representation. These modules are incorporated into a unified method known as CLustering-guided cOntrastiVE fusioN (CLOVEN). We quantitatively and qualitatively evaluate the proposed method on five datasets, demonstrating that CLOVEN outperforms 11 competitive multi-view learning methods in clustering and classification. In the incomplete view scenario, our proposed method resists noise interference better than those of our competitors. Furthermore, the visualization analysis shows that CLOVEN can preserve the intrinsic structure of view-specific representation while also improving the compactness of view-commom representation. Our source code will be available soon at https://github.com/guanzhou-ke/cloven. | ['Yang Yu', 'Yongqi Zhu', 'Chang Xu', 'Chenyang Xu', 'Xiaoli Wang', 'Guoqing Chao', 'Guanzhou Ke'] | 2022-12-28 | null | null | null | null | ['multi-view-learning'] | ['computer-vision'] | [-1.63162857e-01 -3.60779881e-01 -1.67133480e-01 -3.79594535e-01
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c1fbb226-0577-425d-9767-d49c9519db35 | hierarchical-reinforcement-learning-for-5 | null | null | https://openreview.net/forum?id=6IqTG69Lkky | https://openreview.net/pdf?id=6IqTG69Lkky | Hierarchical reinforcement learning for efficent exploration and transfer | Sparse-reward domains are challenging for reinforcement learning algorithms since significant exploration is needed before encountering reward for the first time. Hierarchical reinforcement learning can facilitate exploration by reducing the number of decisions necessary before obtaining a reward. In this paper, we present a novel hierarchical reinforcement learning framework based on the compression of an invariant state space that is common to a range of tasks. The algorithm introduces subtasks which consist in moving between the state partitions induced by the compression. Results indicate that the algorithm can successfully solve complex sparse-reward domains, and transfer knowledge to solve new, previously unseen tasks more quickly. | ['Anders Jonsson', 'Damien Allonsius', 'Simone Totaro', 'Lorenzo Steccanella'] | 2020-06-12 | null | null | null | icml-workshop-lifelongml-2020-7 | ['hierarchical-reinforcement-learning'] | ['methodology'] | [ 3.51512641e-01 2.26037219e-01 -4.30699795e-01 -4.18126509e-02
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912e3628-d6d5-43a7-a00c-70a1db88640a | neural-batch-sampling-with-reinforcement | null | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/5576_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123710749.pdf | Neural Batch Sampling with Reinforcement Learning for Semi-Supervised Anomaly Detection | We are interested in the detection and segmentation of anomalies in images where the anomalies are typically small (i.e., a small tear in woven fabric, broken pin of an IC chip). From a statistical learning point of view, anomalies have low occurrence probability and are not from the main modes of a data distribution. Learning a generative model of anomalous data from a natural distribution of data can be difficult because the data distribution is heavily skewed towards a large amount of non-anomalous data. When training a generative model on such imbalanced data using an iterative learning algorithm like stochastic gradient descent (SGD), we observe an expected yet interesting trend in the loss values (a measure of the learned models performance) after each gradient update across data samples. Naturally, as the model sees more non-anomalous data during training, the loss values over a non-anomalous data sample decreases, while the loss values on an anomalous data sample fluctuates. In this work, our key hypothesis is that this change in loss values during training can be used as a feature to identify anomalous data. In particular, we propose a novel semi-supervised learning algorithm for anomaly detection and segmentation using an anomaly classifier that uses as input the extit{loss profile} of a data sample processed through an autoencoder. The loss profile is defined as a sequence of reconstruction loss values produced during iterative training. To amplify the difference in loss profiles between anomalous and non-anomalous data, we also introduce a Reinforcement Learning based meta-algorithm, which we call the neural batch sampler, to strategically sample training batches during autoencoder training. Experimental results on multiple datasets with a high diversity of textures and objects, often with multiple modes of defects within them, demonstrate the capabilities and effectiveness of our method when compared with existing state-of-the-art baselines. | ['Wen-Hsuan Chu', 'Kris M. Kitani'] | null | null | null | null | eccv-2020-8 | ['supervised-anomaly-detection', 'semi-supervised-anomaly-detection'] | ['computer-vision', 'computer-vision'] | [ 4.46700931e-01 3.16866650e-03 6.73685363e-03 -5.01541913e-01
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7b0df822-7ac8-45bf-bb87-06fa26686c21 | enhanced-graph-learning-schemes-driven-by | 2207.04747 | null | https://arxiv.org/abs/2207.04747v1 | https://arxiv.org/pdf/2207.04747v1.pdf | Enhanced graph-learning schemes driven by similar distributions of motifs | This paper looks at the task of network topology inference, where the goal is to learn an unknown graph from nodal observations. One of the novelties of the approach put forth is the consideration of prior information about the density of motifs of the unknown graph to enhance the inference of classical Gaussian graphical models. Dealing with the density of motifs directly constitutes a challenging combinatorial task. However, we note that if two graphs have similar motif densities, one can show that the expected value of a polynomial applied to their empirical spectral distributions will be similar. Guided by this, we first assume that we have a reference graph that is related to the sought graph (in the sense of having similar motif densities) and then, we exploit this relation by incorporating a similarity constraint and a regularization term in the network topology inference optimization problem. The (non-)convexity of the optimization problem is discussed and a computational efficient alternating majorization-minimization algorithm is designed. We assess the performance of the proposed method through exhaustive numerical experiments where different constraints are considered and compared against popular baselines algorithms on both synthetic and real-world datasets. | ['Antonio G. Marques', 'Santiago Segarra', 'T. Mitchell Roddenberry', 'Samuel Rey'] | 2022-07-11 | null | null | null | null | ['inference-optimization'] | ['audio'] | [ 3.85244548e-01 3.50052416e-01 -9.87889431e-03 -1.20236680e-01
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b3c8b90c-482f-4df2-ac8b-a4da6e36b854 | position-aware-tagging-for-aspect-sentiment | 2010.02609 | null | https://arxiv.org/abs/2010.02609v3 | https://arxiv.org/pdf/2010.02609v3.pdf | Position-Aware Tagging for Aspect Sentiment Triplet Extraction | Aspect Sentiment Triplet Extraction (ASTE) is the task of extracting the triplets of target entities, their associated sentiment, and opinion spans explaining the reason for the sentiment. Existing research efforts mostly solve this problem using pipeline approaches, which break the triplet extraction process into several stages. Our observation is that the three elements within a triplet are highly related to each other, and this motivates us to build a joint model to extract such triplets using a sequence tagging approach. However, how to effectively design a tagging approach to extract the triplets that can capture the rich interactions among the elements is a challenging research question. In this work, we propose the first end-to-end model with a novel position-aware tagging scheme that is capable of jointly extracting the triplets. Our experimental results on several existing datasets show that jointly capturing elements in the triplet using our approach leads to improved performance over the existing approaches. We also conducted extensive experiments to investigate the model effectiveness and robustness. | ['Lidong Bing', 'Wei Lu', 'Hao Li', 'Lu Xu'] | 2020-10-06 | null | https://aclanthology.org/2020.emnlp-main.183 | https://aclanthology.org/2020.emnlp-main.183.pdf | emnlp-2020-11 | ['aspect-sentiment-triplet-extraction'] | ['natural-language-processing'] | [ 1.79051384e-01 -1.21447213e-01 -1.33583143e-01 -5.35123289e-01
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f183da51-40fb-4092-8bea-15b90ba1c9dc | a-general-framework-for-revealing-human-mind | 2102.05236 | null | https://arxiv.org/abs/2102.05236v1 | https://arxiv.org/pdf/2102.05236v1.pdf | A General Framework for Revealing Human Mind with auto-encoding GANs | Addressing the question of visualising human mind could help us to find regions that are associated with observed cognition and responsible for expressing the elusive mental image, leading to a better understanding of cognitive function. The traditional approach treats brain decoding as a classification problem, reading the mind through statistical analysis of brain activity. However, human thought is rich and varied, that it is often influenced by more of a combination of object features than a specific type of category. For this reason, we propose an end-to-end brain decoding framework which translates brain activity into an image by latent space alignment. To find the correspondence from brain signal features to image features, we embedded them into two latent spaces with modality-specific encoders and then aligned the two spaces by minimising the distance between paired latent representations. The proposed framework was trained by simultaneous electroencephalogram and functional MRI data, which were recorded when the subjects were viewing or imagining a set of image stimuli. In this paper, we focused on implementing the fMRI experiment. Our experimental results demonstrated the feasibility of translating brain activity to an image. The reconstructed image matches image stimuli approximate in both shape and colour. Our framework provides a promising direction for building a direct visualisation to reveal human mind. | ['Yike Guo', 'Peter Childs', 'Chunlin Li', 'Jialu Fan', 'Wenjia Bai', 'Ling Li', 'Shuo Wang', 'Rui Zhou', 'Pan Wang'] | 2021-02-10 | null | null | null | null | ['brain-decoding', 'brain-decoding'] | ['medical', 'miscellaneous'] | [ 6.47394300e-01 8.18600506e-02 3.53138655e-01 -5.70294976e-01
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04c67273-73de-40df-a702-66b27d6be858 | ostec-one-shot-texture-completion | 2012.15370 | null | https://arxiv.org/abs/2012.15370v1 | https://arxiv.org/pdf/2012.15370v1.pdf | OSTeC: One-Shot Texture Completion | The last few years have witnessed the great success of non-linear generative models in synthesizing high-quality photorealistic face images. Many recent 3D facial texture reconstruction and pose manipulation from a single image approaches still rely on large and clean face datasets to train image-to-image Generative Adversarial Networks (GANs). Yet the collection of such a large scale high-resolution 3D texture dataset is still very costly and difficult to maintain age/ethnicity balance. Moreover, regression-based approaches suffer from generalization to the in-the-wild conditions and are unable to fine-tune to a target-image. In this work, we propose an unsupervised approach for one-shot 3D facial texture completion that does not require large-scale texture datasets, but rather harnesses the knowledge stored in 2D face generators. The proposed approach rotates an input image in 3D and fill-in the unseen regions by reconstructing the rotated image in a 2D face generator, based on the visible parts. Finally, we stitch the most visible textures at different angles in the UV image-plane. Further, we frontalize the target image by projecting the completed texture into the generator. The qualitative and quantitative experiments demonstrate that the completed UV textures and frontalized images are of high quality, resembles the original identity, can be used to train a texture GAN model for 3DMM fitting and improve pose-invariant face recognition. | ['Stefanos Zafeiriou', 'Jiankang Deng', 'Baris Gecer'] | 2020-12-30 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Gecer_OSTeC_One-Shot_Texture_Completion_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Gecer_OSTeC_One-Shot_Texture_Completion_CVPR_2021_paper.pdf | cvpr-2021-1 | ['robust-face-recognition'] | ['computer-vision'] | [ 4.98552591e-01 3.19561541e-01 1.77456334e-01 -3.29169303e-01
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b9ccb583-31f5-4489-83b4-a6b0a0ede0db | a-generally-applicable-highly-scalable | 1711.05508 | null | http://arxiv.org/abs/1711.05508v1 | http://arxiv.org/pdf/1711.05508v1.pdf | A Generally Applicable, Highly Scalable Measurement Computation and Optimization Approach to Sequential Model-Based Diagnosis | Model-Based Diagnosis deals with the identification of the real cause of a
system's malfunction based on a formal system model and observations of the
system behavior. When a malfunction is detected, there is usually not enough
information available to pinpoint the real cause and one needs to discriminate
between multiple fault hypotheses (called diagnoses). To this end, Sequential
Diagnosis approaches ask an oracle for additional system measurements.
This work presents strategies for (optimal) measurement selection in
model-based sequential diagnosis. In particular, assuming a set of leading
diagnoses being given, we show how queries (sets of measurements) can be
computed and optimized along two dimensions: expected number of queries and
cost per query. By means of a suitable decoupling of two optimizations and a
clever search space reduction the computations are done without any inference
engine calls. For the full search space, we give a method requiring only a
polynomial number of inferences and show how query properties can be guaranteed
which existing methods do not provide. Evaluation results using real-world
problems indicate that the new method computes (virtually) optimal queries
instantly independently of the size and complexity of the considered diagnosis
problems and outperforms equally general methods not exploiting the proposed
theory by orders of magnitude. | ['Wolfgang Schmid', 'Konstantin Schekotihin', 'Patrick Rodler'] | 2017-11-15 | null | null | null | null | ['sequential-diagnosis'] | ['medical'] | [ 4.05893892e-01 3.80697995e-01 8.31827521e-02 -7.14470968e-02
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ca63af98-bff6-4c9b-8955-9023769c97d0 | neuro-symbolic-sudoku-solver | 2307.00653 | null | https://arxiv.org/abs/2307.00653v1 | https://arxiv.org/pdf/2307.00653v1.pdf | Neuro-Symbolic Sudoku Solver | Deep Neural Networks have achieved great success in some of the complex tasks that humans can do with ease. These include image recognition/classification, natural language processing, game playing etc. However, modern Neural Networks fail or perform poorly when trained on tasks that can be solved easily using backtracking and traditional algorithms. Therefore, we use the architecture of the Neuro Logic Machine (NLM) and extend its functionality to solve a 9X9 game of Sudoku. To expand the application of NLMs, we generate a random grid of cells from a dataset of solved games and assign up to 10 new empty cells. The goal of the game is then to find a target value ranging from 1 to 9 and fill in the remaining empty cells while maintaining a valid configuration. In our study, we showcase an NLM which is capable of obtaining 100% accuracy for solving a Sudoku with empty cells ranging from 3 to 10. The purpose of this study is to demonstrate that NLMs can also be used for solving complex problems and games like Sudoku. We also analyze the behaviour of NLMs with a backtracking algorithm by comparing the convergence time using a graph plot on the same problem. With this study we show that Neural Logic Machines can be trained on the tasks that traditional Deep Learning architectures fail using Reinforcement Learning. We also aim to propose the importance of symbolic learning in explaining the systematicity in the hybrid model of NLMs. | ['Lalit Pandey', 'Ashutosh Hathidara'] | 2023-07-02 | null | null | null | null | ['suduko'] | ['playing-games'] | [ 1.85726479e-01 2.52151072e-01 7.65239447e-02 9.94361704e-04
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325d219e-fc84-494b-b592-44a6f446bfc2 | scalable-semi-supervised-dimensionality | 2201.00701 | null | https://arxiv.org/abs/2201.00701v1 | https://arxiv.org/pdf/2201.00701v1.pdf | Scalable semi-supervised dimensionality reduction with GPU-accelerated EmbedSOM | Dimensionality reduction methods have found vast application as visualization tools in diverse areas of science. Although many different methods exist, their performance is often insufficient for providing quick insight into many contemporary datasets, and the unsupervised mode of use prevents the users from utilizing the methods for dataset exploration and fine-tuning the details for improved visualization quality. We present BlosSOM, a high-performance semi-supervised dimensionality reduction software for interactive user-steerable visualization of high-dimensional datasets with millions of individual data points. BlosSOM builds on a GPU-accelerated implementation of the EmbedSOM algorithm, complemented by several landmark-based algorithms for interfacing the unsupervised model learning algorithms with the user supervision. We show the application of BlosSOM on realistic datasets, where it helps to produce high-quality visualizations that incorporate user-specified layout and focus on certain features. We believe the semi-supervised dimensionality reduction will improve the data visualization possibilities for science areas such as single-cell cytometry, and provide a fast and efficient base methodology for new directions in dataset exploration and annotation. | ['Jiří Vondrášek', 'Martin Kruliš', 'Jan Musil', 'Abhishek Koladiya', 'Miroslav Kratochvíl', 'Soňa Molnárová', 'Adam Šmelko'] | 2022-01-03 | null | null | null | null | ['supervised-dimensionality-reduction'] | ['computer-vision'] | [-1.06666878e-01 -3.56686741e-01 1.95613325e-01 -3.56231123e-01
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4ea8eea0-ce3c-4825-890c-6eb7ebd2d501 | adversary-aware-rumor-detection | null | null | https://aclanthology.org/2021.findings-acl.118 | https://aclanthology.org/2021.findings-acl.118.pdf | Adversary-Aware Rumor Detection | null | ['Hong-Han Shuai', 'Shao-Yu Weng', 'Yi-Ting Chang', 'Yi-Syuan Chen', 'Yun-Zhu Song'] | null | null | null | null | findings-acl-2021-8 | ['rumour-detection'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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ffa3a2e3-99e2-4294-ade8-27a169b55cd0 | tokencut-segmenting-objects-in-images-and | 2209.00383 | null | https://arxiv.org/abs/2209.00383v2 | https://arxiv.org/pdf/2209.00383v2.pdf | TokenCut: Segmenting Objects in Images and Videos with Self-supervised Transformer and Normalized Cut | In this paper, we describe a graph-based algorithm that uses the features obtained by a self-supervised transformer to detect and segment salient objects in images and videos. With this approach, the image patches that compose an image or video are organised into a fully connected graph, where the edge between each pair of patches is labeled with a similarity score between patches using features learned by the transformer. Detection and segmentation of salient objects is then formulated as a graph-cut problem and solved using the classical Normalized Cut algorithm. Despite the simplicity of this approach, it achieves state-of-the-art results on several common image and video detection and segmentation tasks. For unsupervised object discovery, this approach outperforms the competing approaches by a margin of 6.1%, 5.7%, and 2.6%, respectively, when tested with the VOC07, VOC12, and COCO20K datasets. For the unsupervised saliency detection task in images, this method improves the score for Intersection over Union (IoU) by 4.4%, 5.6% and 5.2%. When tested with the ECSSD, DUTS, and DUT-OMRON datasets, respectively, compared to current state-of-the-art techniques. This method also achieves competitive results for unsupervised video object segmentation tasks with the DAVIS, SegTV2, and FBMS datasets. | ['Dominique Vaufreydaz', 'James L Crowley', 'Shell Xu Hu', 'Maomao Li', 'Yuming Du', 'Yuan Yuan', 'Xi Shen', 'Yangtao Wang'] | 2022-09-01 | null | null | null | null | ['object-discovery', 'saliency-detection', 'unsupervised-video-object-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 5.31125605e-01 2.50858426e-01 -3.35434735e-01 -1.27688870e-01
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7b1f4758-c4d4-498f-b1eb-8641bf7e101a | srnet-improving-generalization-in-3d-human | 2007.09389 | null | https://arxiv.org/abs/2007.09389v1 | https://arxiv.org/pdf/2007.09389v1.pdf | SRNet: Improving Generalization in 3D Human Pose Estimation with a Split-and-Recombine Approach | Human poses that are rare or unseen in a training set are challenging for a network to predict. Similar to the long-tailed distribution problem in visual recognition, the small number of examples for such poses limits the ability of networks to model them. Interestingly, local pose distributions suffer less from the long-tail problem, i.e., local joint configurations within a rare pose may appear within other poses in the training set, making them less rare. We propose to take advantage of this fact for better generalization to rare and unseen poses. To be specific, our method splits the body into local regions and processes them in separate network branches, utilizing the property that a joint position depends mainly on the joints within its local body region. Global coherence is maintained by recombining the global context from the rest of the body into each branch as a low-dimensional vector. With the reduced dimensionality of less relevant body areas, the training set distribution within network branches more closely reflects the statistics of local poses instead of global body poses, without sacrificing information important for joint inference. The proposed split-and-recombine approach, called SRNet, can be easily adapted to both single-image and temporal models, and it leads to appreciable improvements in the prediction of rare and unseen poses. | ['Minhao Liu', 'Xiao Sun', 'Stephen Lin', 'Fuyang Huang', 'Ailing Zeng', 'Qiang Xu'] | 2020-07-18 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2201_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123590494.pdf | eccv-2020-8 | ['monocular-3d-human-pose-estimation'] | ['computer-vision'] | [-7.41012096e-02 1.08036913e-01 -5.43418467e-01 -3.17516059e-01
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451ff3a9-1ea0-40b3-bcf9-2f9995dcd503 | bifocal-neural-asr-exploiting-keyword | 2108.01704 | null | https://arxiv.org/abs/2108.01704v1 | https://arxiv.org/pdf/2108.01704v1.pdf | Bifocal Neural ASR: Exploiting Keyword Spotting for Inference Optimization | We present Bifocal RNN-T, a new variant of the Recurrent Neural Network Transducer (RNN-T) architecture designed for improved inference time latency on speech recognition tasks. The architecture enables a dynamic pivot for its runtime compute pathway, namely taking advantage of keyword spotting to select which component of the network to execute for a given audio frame. To accomplish this, we leverage a recurrent cell we call the Bifocal LSTM (BFLSTM), which we detail in the paper. The architecture is compatible with other optimization strategies such as quantization, sparsification, and applying time-reduction layers, making it especially applicable for deployed, real-time speech recognition settings. We present the architecture and report comparative experimental results on voice-assistant speech recognition tasks. Specifically, we show our proposed Bifocal RNN-T can improve inference cost by 29.1% with matching word error rates and only a minor increase in memory size. | ['Ariya Rastrow', 'Grant P. Strimel', 'Jonathan Macoskey'] | 2021-08-03 | null | null | null | null | ['inference-optimization'] | ['audio'] | [ 4.59263682e-01 1.96350962e-01 -2.07333729e-01 -2.67807156e-01
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e9027936-335c-4401-988c-4160449c5900 | da4ad-end-to-end-deep-attention-aware | 2003.03026 | null | https://arxiv.org/abs/2003.03026v2 | https://arxiv.org/pdf/2003.03026v2.pdf | DA4AD: End-to-End Deep Attention-based Visual Localization for Autonomous Driving | We present a visual localization framework based on novel deep attention aware features for autonomous driving that achieves centimeter level localization accuracy. Conventional approaches to the visual localization problem rely on handcrafted features or human-made objects on the road. They are known to be either prone to unstable matching caused by severe appearance or lighting changes, or too scarce to deliver constant and robust localization results in challenging scenarios. In this work, we seek to exploit the deep attention mechanism to search for salient, distinctive and stable features that are good for long-term matching in the scene through a novel end-to-end deep neural network. Furthermore, our learned feature descriptors are demonstrated to be competent to establish robust matches and therefore successfully estimate the optimal camera poses with high precision. We comprehensively validate the effectiveness of our method using a freshly collected dataset with high-quality ground truth trajectories and hardware synchronization between sensors. Results demonstrate that our method achieves a competitive localization accuracy when compared to the LiDAR-based localization solutions under various challenging circumstances, leading to a potential low-cost localization solution for autonomous driving. | ['Guowei Wan', 'Xiaofei Rui', 'Li Yu', 'Shiyu Song', 'Gang Wang', 'Shenhua Hou', 'Yao Zhou'] | 2020-03-06 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/6091_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123730273.pdf | eccv-2020-8 | ['deep-attention', 'deep-attention'] | ['computer-vision', 'natural-language-processing'] | [-3.48983467e-01 -4.48532671e-01 -1.35750294e-01 -5.82878590e-01
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15868074-fc3e-48bd-9110-23cd42a3c087 | sparse-representation-classification-with | null | null | http://openaccess.thecvf.com/content_cvpr_2015/html/Zhang_Sparse_Representation_Classification_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Zhang_Sparse_Representation_Classification_2015_CVPR_paper.pdf | Sparse Representation Classification With Manifold Constraints Transfer | The fact that image data samples lie on a manifold has been successfully exploited in many learning and inference problems. In this paper we leverage the specific structure of data in order to improve recognition accuracies in general recognition tasks. In particular we propose a novel framework that allows to embed manifold priors into sparse representation-based classification (SRC) approaches. We also show that manifold constraints can be transferred from the data to the optimized variables if these are linearly correlated. Using this new insight, we define an efficient alternating direction method of multipliers (ADMM) that can consistently integrate the manifold constraints during the optimization process. This is based on the property that we can recast the problem as the projection over the manifold via a linear embedding method based on the Geodesic distance. The proposed approach is successfully applied on face, digit, action and objects recognition showing a consistently increase on performance when compared to the state of the art. | ['Alessio Del Bue', 'Alessandro Perina', 'Vittorio Murino', 'Baochang Zhang'] | 2015-06-01 | null | null | null | cvpr-2015-6 | ['sparse-representation-based-classification'] | ['computer-vision'] | [ 2.86227763e-01 -1.90024897e-02 -2.47144759e-01 -5.22460938e-01
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4085b317-537a-44d1-997c-d2ab38919fed | event-causality-identification-with-causal | 2211.12154 | null | https://arxiv.org/abs/2211.12154v1 | https://arxiv.org/pdf/2211.12154v1.pdf | Event Causality Identification with Causal News Corpus -- Shared Task 3, CASE 2022 | The Event Causality Identification Shared Task of CASE 2022 involved two subtasks working on the Causal News Corpus. Subtask 1 required participants to predict if a sentence contains a causal relation or not. This is a supervised binary classification task. Subtask 2 required participants to identify the Cause, Effect and Signal spans per causal sentence. This could be seen as a supervised sequence labeling task. For both subtasks, participants uploaded their predictions for a held-out test set, and ranking was done based on binary F1 and macro F1 scores for Subtask 1 and 2, respectively. This paper summarizes the work of the 17 teams that submitted their results to our competition and 12 system description papers that were received. The best F1 scores achieved for Subtask 1 and 2 were 86.19% and 54.15%, respectively. All the top-performing approaches involved pre-trained language models fine-tuned to the targeted task. We further discuss these approaches and analyze errors across participants' systems in this paper. | ['Nelleke Oostdijk', 'Farhana Ferdousi Liza', 'Onur Uca', 'Tommaso Caselli', 'Ali Hürriyetoğlu', 'Hansi Hettiarachchi', 'Fiona Anting Tan'] | 2022-11-22 | null | null | null | null | ['event-causality-identification'] | ['natural-language-processing'] | [ 5.41451633e-01 5.57900250e-01 -4.20702428e-01 -6.19315326e-01
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49ed0ebb-179d-4966-a6d4-d7ea91ff1be1 | you-only-demonstrate-once-category-level | 2201.12716 | null | https://arxiv.org/abs/2201.12716v2 | https://arxiv.org/pdf/2201.12716v2.pdf | You Only Demonstrate Once: Category-Level Manipulation from Single Visual Demonstration | Promising results have been achieved recently in category-level manipulation that generalizes across object instances. Nevertheless, it often requires expensive real-world data collection and manual specification of semantic keypoints for each object category and task. Additionally, coarse keypoint predictions and ignoring intermediate action sequences hinder adoption in complex manipulation tasks beyond pick-and-place. This work proposes a novel, category-level manipulation framework that leverages an object-centric, category-level representation and model-free 6 DoF motion tracking. The canonical object representation is learned solely in simulation and then used to parse a category-level, task trajectory from a single demonstration video. The demonstration is reprojected to a target trajectory tailored to a novel object via the canonical representation. During execution, the manipulation horizon is decomposed into longrange, collision-free motion and last-inch manipulation. For the latter part, a category-level behavior cloning (CatBC) method leverages motion tracking to perform closed-loop control. CatBC follows the target trajectory, projected from the demonstration and anchored to a dynamically selected category-level coordinate frame. The frame is automatically selected along the manipulation horizon by a local attention mechanism. This framework allows to teach different manipulation strategies by solely providing a single demonstration, without complicated manual programming. Extensive experiments demonstrate its efficacy in a range of challenging industrial tasks in highprecision assembly, which involve learning complex, long-horizon policies. The process exhibits robustness against uncertainty due to dynamics as well as generalization across object instances and scene configurations. The supplementary video is available at https://www.youtube.com/watch?v=WAr8ZY3mYyw | ['Stefan Schaal', 'Kostas Bekris', 'Wenzhao Lian', 'Bowen Wen'] | 2022-01-30 | null | null | null | null | ['3d-object-tracking', 'industrial-robots', 'robotic-grasping', 'robot-task-planning'] | ['computer-vision', 'robots', 'robots', 'robots'] | [-8.82530883e-02 -2.37466201e-01 -4.80738163e-01 -1.04532659e-01
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cb4ed976-b71f-4b03-8e90-55d26fbac8ec | uncertainty-aware-multimodal-activity | 1811.10811 | null | https://arxiv.org/abs/1811.10811v3 | https://arxiv.org/pdf/1811.10811v3.pdf | Uncertainty aware audiovisual activity recognition using deep Bayesian variational inference | Deep neural networks (DNNs) provide state-of-the-art results for a multitude of applications, but the approaches using DNNs for multimodal audiovisual applications do not consider predictive uncertainty associated with individual modalities. Bayesian deep learning methods provide principled confidence and quantify predictive uncertainty. Our contribution in this work is to propose an uncertainty aware multimodal Bayesian fusion framework for activity recognition. We demonstrate a novel approach that combines deterministic and variational layers to scale Bayesian DNNs to deeper architectures. Our experiments using in- and out-of-distribution samples selected from a subset of Moments-in-Time (MiT) dataset show a more reliable confidence measure as compared to the non-Bayesian baseline and the Monte Carlo dropout (MC dropout) approximate Bayesian inference. We also demonstrate the uncertainty estimates obtained from the proposed framework can identify out-of-distribution data on the UCF101 and MiT datasets. In the multimodal setting, the proposed framework improved precision-recall AUC by 10.2% on the subset of MiT dataset as compared to non-Bayesian baseline. | ['Jonathan Huang', 'Paulo Lopez Meyer', 'Omesh Tickoo', 'Mahesh Subedar', 'Ranganath Krishnan'] | 2018-11-27 | null | null | null | null | ['multimodal-activity-recognition'] | ['computer-vision'] | [-1.16136044e-01 9.27367732e-02 -5.75845800e-02 -7.72146225e-01
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abf085ca-b9d3-4f11-a9fc-398b52c6a684 | fine-tuning-multi-hop-question-answering-with | 2004.13821 | null | https://arxiv.org/abs/2004.13821v3 | https://arxiv.org/pdf/2004.13821v3.pdf | Fine-tuning Multi-hop Question Answering with Hierarchical Graph Network | In this paper, we present a two stage model for multi-hop question answering. The first stage is a hierarchical graph network, which is used to reason over multi-hop question and is capable to capture different levels of granularity using the nature structure(i.e., paragraphs, questions, sentences and entities) of documents. The reasoning process is convert to node classify task(i.e., paragraph nodes and sentences nodes). The second stage is a language model fine-tuning task. In a word, stage one use graph neural network to select and concatenate support sentences as one paragraph, and stage two find the answer span in language model fine-tuning paradigm. | ['Guanming Xiong'] | 2020-04-20 | null | null | null | null | ['multi-hop-question-answering'] | ['knowledge-base'] | [-1.17746647e-03 6.02588177e-01 -2.84785926e-01 -4.90328848e-01
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319ad2fd-c307-428b-ac78-dd81bc8e0f02 | action2motion-conditioned-generation-of-3d | 2007.15240 | null | https://arxiv.org/abs/2007.15240v1 | https://arxiv.org/pdf/2007.15240v1.pdf | Action2Motion: Conditioned Generation of 3D Human Motions | Action recognition is a relatively established task, where givenan input sequence of human motion, the goal is to predict its ac-tion category. This paper, on the other hand, considers a relativelynew problem, which could be thought of as an inverse of actionrecognition: given a prescribed action type, we aim to generateplausible human motion sequences in 3D. Importantly, the set ofgenerated motions are expected to maintain itsdiversityto be ableto explore the entire action-conditioned motion space; meanwhile,each sampled sequence faithfully resembles anaturalhuman bodyarticulation dynamics. Motivated by these objectives, we followthe physics law of human kinematics by adopting the Lie Algebratheory to represent thenaturalhuman motions; we also propose atemporal Variational Auto-Encoder (VAE) that encourages adiversesampling of the motion space. A new 3D human motion dataset, HumanAct12, is also constructed. Empirical experiments overthree distinct human motion datasets (including ours) demonstratethe effectiveness of our approach. | ['Xinxin Zuo', 'Sen Wang', 'Chuan Guo', 'Shihao Zou', 'Li Cheng', 'Annan Deng', 'Minglun Gong', 'Qingyao Sun'] | 2020-07-30 | null | null | null | null | ['human-action-generation', 'action-generation'] | ['computer-vision', 'computer-vision'] | [ 8.85632075e-03 2.91619003e-01 -5.80047548e-01 8.57461467e-02
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1c06deeb-e709-418c-9e04-472e45798fb5 | unsupervised-neural-adaptation-model-based-on | 2012.13152 | null | https://arxiv.org/abs/2012.13152v1 | https://arxiv.org/pdf/2012.13152v1.pdf | Unsupervised neural adaptation model based on optimal transport for spoken language identification | Due to the mismatch of statistical distributions of acoustic speech between training and testing sets, the performance of spoken language identification (SLID) could be drastically degraded. In this paper, we propose an unsupervised neural adaptation model to deal with the distribution mismatch problem for SLID. In our model, we explicitly formulate the adaptation as to reduce the distribution discrepancy on both feature and classifier for training and testing data sets. Moreover, inspired by the strong power of the optimal transport (OT) to measure distribution discrepancy, a Wasserstein distance metric is designed in the adaptation loss. By minimizing the classification loss on the training data set with the adaptation loss on both training and testing data sets, the statistical distribution difference between training and testing domains is reduced. We carried out SLID experiments on the oriental language recognition (OLR) challenge data corpus where the training and testing data sets were collected from different conditions. Our results showed that significant improvements were achieved on the cross domain test tasks. | ['Hisashi Kawai', 'Yu Tsao', 'Peng Shen', 'Xugang Lu'] | 2020-12-24 | null | null | null | null | ['spoken-language-identification'] | ['speech'] | [ 1.14422895e-01 1.23567628e-02 1.75662920e-01 -8.50969851e-01
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9c14bfcc-d3f4-4d66-a8a6-7c397d46b272 | edge-data-based-trailer-inception | 2202.10236 | null | https://arxiv.org/abs/2202.10236v1 | https://arxiv.org/pdf/2202.10236v1.pdf | Edge Data Based Trailer Inception Probabilistic Matrix Factorization for Context-Aware Movie Recommendation | The rapid growth of edge data generated by mobile devices and applications deployed at the edge of the network has exacerbated the problem of information overload. As an effective way to alleviate information overload, recommender system can improve the quality of various services by adding application data generated by users on edge devices, such as visual and textual information, on the basis of sparse rating data. The visual information in the movie trailer is a significant part of the movie recommender system. However, due to the complexity of visual information extraction, data sparsity cannot be remarkably alleviated by merely using the rough visual features to improve the rating prediction accuracy. Fortunately, the convolutional neural network can be used to extract the visual features precisely. Therefore, the end-to-end neural image caption (NIC) model can be utilized to obtain the textual information describing the visual features of movie trailers. This paper proposes a trailer inception probabilistic matrix factorization model called Ti-PMF, which combines NIC, recurrent convolutional neural network, and probabilistic matrix factorization models as the rating prediction model. We implement the proposed Ti-PMF model with extensive experiments on three real-world datasets to validate its effectiveness. The experimental results illustrate that the proposed Ti-PMF outperforms the existing ones. | ['Feng Xia', 'Abdul Aziz', 'Ge Xu', 'Junjian Li', 'Zhichen Ni', 'Zhu Wang', 'Zhe Li', 'Honglong Chen'] | 2022-02-16 | null | null | null | null | ['movie-recommendation'] | ['miscellaneous'] | [-1.88997298e-01 -6.13482237e-01 -4.15326327e-01 -3.24923784e-01
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bda7b8c0-5ee0-46bf-9e09-f30c4e1e7b36 | high-dimensional-bayesian-optimization-with-4 | 2204.13753 | null | https://arxiv.org/abs/2204.13753v2 | https://arxiv.org/pdf/2204.13753v2.pdf | High Dimensional Bayesian Optimization with Kernel Principal Component Analysis | Bayesian Optimization (BO) is a surrogate-based global optimization strategy that relies on a Gaussian Process regression (GPR) model to approximate the objective function and an acquisition function to suggest candidate points. It is well-known that BO does not scale well for high-dimensional problems because the GPR model requires substantially more data points to achieve sufficient accuracy and acquisition optimization becomes computationally expensive in high dimensions. Several recent works aim at addressing these issues, e.g., methods that implement online variable selection or conduct the search on a lower-dimensional sub-manifold of the original search space. Advancing our previous work of PCA-BO that learns a linear sub-manifold, this paper proposes a novel kernel PCA-assisted BO (KPCA-BO) algorithm, which embeds a non-linear sub-manifold in the search space and performs BO on this sub-manifold. Intuitively, constructing the GPR model on a lower-dimensional sub-manifold helps improve the modeling accuracy without requiring much more data from the objective function. Also, our approach defines the acquisition function on the lower-dimensional sub-manifold, making the acquisition optimization more manageable. We compare the performance of KPCA-BO to a vanilla BO and to PCA-BO on the multi-modal problems of the COCO/BBOB benchmark suite. Empirical results show that KPCA-BO outperforms BO in terms of convergence speed on most test problems, and this benefit becomes more significant when the dimensionality increases. For the 60D functions, KPCA-BO achieves better results than PCA-BO for many test cases. Compared to the vanilla BO, it efficiently reduces the CPU time required to train the GPR model and to optimize the acquisition function compared to the vanilla BO. | ['Carola Doerr', 'Hao Wang', 'Elena Raponi', 'Kirill Antonov'] | 2022-04-28 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [-4.98305351e-01 -3.57309461e-01 -1.31931901e-01 -8.14846978e-02
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b92d6978-809d-4de3-88e4-46f0550f09d9 | hit-and-lead-discovery-with-explorative-rl | 2110.01219 | null | https://arxiv.org/abs/2110.01219v3 | https://arxiv.org/pdf/2110.01219v3.pdf | Hit and Lead Discovery with Explorative RL and Fragment-based Molecule Generation | Recently, utilizing reinforcement learning (RL) to generate molecules with desired properties has been highlighted as a promising strategy for drug design. A molecular docking program - a physical simulation that estimates protein-small molecule binding affinity - can be an ideal reward scoring function for RL, as it is a straightforward proxy of the therapeutic potential. Still, two imminent challenges exist for this task. First, the models often fail to generate chemically realistic and pharmacochemically acceptable molecules. Second, the docking score optimization is a difficult exploration problem that involves many local optima and less smooth surfaces with respect to molecular structure. To tackle these challenges, we propose a novel RL framework that generates pharmacochemically acceptable molecules with large docking scores. Our method - Fragment-based generative RL with Explorative Experience replay for Drug design (FREED) - constrains the generated molecules to a realistic and qualified chemical space and effectively explores the space to find drugs by coupling our fragment-based generation method and a novel error-prioritized experience replay (PER). We also show that our model performs well on both de novo and scaffold-based schemes. Our model produces molecules of higher quality compared to existing methods while achieving state-of-the-art performance on two of three targets in terms of the docking scores of the generated molecules. We further show with ablation studies that our method, predictive error-PER (FREED(PE)), significantly improves the model performance. | ['Sung Ju Hwang', 'Seongok Ryu', 'Seul Lee', 'Doyeong Hwang', 'Soojung Yang'] | 2021-10-04 | null | http://proceedings.neurips.cc/paper/2021/hash/41da609c519d77b29be442f8c1105647-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/41da609c519d77b29be442f8c1105647-Paper.pdf | neurips-2021-12 | ['molecular-docking'] | ['medical'] | [ 1.18182868e-01 -4.46661143e-03 -3.01420420e-01 1.73503920e-01
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6812562e-aaac-4416-9b2e-406715510145 | ffpdg-fast-fair-and-private-data-generation | 2307.00161 | null | https://arxiv.org/abs/2307.00161v1 | https://arxiv.org/pdf/2307.00161v1.pdf | FFPDG: Fast, Fair and Private Data Generation | Generative modeling has been used frequently in synthetic data generation. Fairness and privacy are two big concerns for synthetic data. Although Recent GAN [\cite{goodfellow2014generative}] based methods show good results in preserving privacy, the generated data may be more biased. At the same time, these methods require high computation resources. In this work, we design a fast, fair, flexible and private data generation method. We show the effectiveness of our method theoretically and empirically. We show that models trained on data generated by the proposed method can perform well (in inference stage) on real application scenarios. | ['Bo wang', 'Francis Iannacci', 'Jinjin Zhao', 'Weijie Xu'] | 2023-06-30 | null | null | null | null | ['fairness', 'synthetic-data-generation', 'synthetic-data-generation', 'fairness'] | ['computer-vision', 'medical', 'miscellaneous', 'miscellaneous'] | [ 6.90656826e-02 3.96184653e-01 -1.89895347e-01 -5.58780253e-01
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0ae62f8f-eddb-4579-8f36-6d29dd6a26be | carime-unpaired-caricature-generation-with | 2010.00246 | null | https://arxiv.org/abs/2010.00246v1 | https://arxiv.org/pdf/2010.00246v1.pdf | CariMe: Unpaired Caricature Generation with Multiple Exaggerations | Caricature generation aims to translate real photos into caricatures with artistic styles and shape exaggerations while maintaining the identity of the subject. Different from the generic image-to-image translation, drawing a caricature automatically is a more challenging task due to the existence of various spacial deformations. Previous caricature generation methods are obsessed with predicting definite image warping from a given photo while ignoring the intrinsic representation and distribution for exaggerations in caricatures. This limits their ability on diverse exaggeration generation. In this paper, we generalize the caricature generation problem from instance-level warping prediction to distribution-level deformation modeling. Based on this assumption, we present the first exploration for unpaired CARIcature generation with Multiple Exaggerations (CariMe). Technically, we propose a Multi-exaggeration Warper network to learn the distribution-level mapping from photo to facial exaggerations. This makes it possible to generate diverse and reasonable exaggerations from randomly sampled warp codes given one input photo. To better represent the facial exaggeration and produce fine-grained warping, a deformation-field-based warping method is also proposed, which helps us to capture more detailed exaggerations than other point-based warping methods. Experiments and two perceptual studies prove the superiority of our method comparing with other state-of-the-art methods, showing the improvement of our work on caricature generation. | ['Zheng Gu', 'Yang Gao', 'Wenbin Li', 'Jing Huo', 'Chuanqi Dong'] | 2020-10-01 | null | null | null | null | ['caricature'] | ['computer-vision'] | [ 3.63715500e-01 3.08015883e-01 -1.59560479e-02 -2.78088540e-01
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ec2e8244-4343-49bf-9377-caf782eee8ea | artfacepoints-high-resolution-facial-landmark | 2210.09204 | null | https://arxiv.org/abs/2210.09204v1 | https://arxiv.org/pdf/2210.09204v1.pdf | ArtFacePoints: High-resolution Facial Landmark Detection in Paintings and Prints | Facial landmark detection plays an important role for the similarity analysis in artworks to compare portraits of the same or similar artists. With facial landmarks, portraits of different genres, such as paintings and prints, can be automatically aligned using control-point-based image registration. We propose a deep-learning-based method for facial landmark detection in high-resolution images of paintings and prints. It divides the task into a global network for coarse landmark prediction and multiple region networks for precise landmark refinement in regions of the eyes, nose, and mouth that are automatically determined based on the predicted global landmark coordinates. We created a synthetically augmented facial landmark art dataset including artistic style transfer and geometric landmark shifts. Our method demonstrates an accurate detection of the inner facial landmarks for our high-resolution dataset of artworks while being comparable for a public low-resolution artwork dataset in comparison to competing methods. | ['Vincent Christlein', 'Andreas Maier', 'Aline Sindel'] | 2022-10-17 | null | null | null | null | ['facial-landmark-detection'] | ['computer-vision'] | [ 3.43143225e-01 -1.25019222e-01 -5.65773919e-02 -6.05521619e-01
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2996f575-85bc-4aa2-974f-67c2584f4cb3 | towards-trustworthy-deception-detection | 2104.11761 | null | https://arxiv.org/abs/2104.11761v1 | https://arxiv.org/pdf/2104.11761v1.pdf | Towards Trustworthy Deception Detection: Benchmarking Model Robustness across Domains, Modalities, and Languages | Evaluating model robustness is critical when developing trustworthy models not only to gain deeper understanding of model behavior, strengths, and weaknesses, but also to develop future models that are generalizable and robust across expected environments a model may encounter in deployment. In this paper we present a framework for measuring model robustness for an important but difficult text classification task - deceptive news detection. We evaluate model robustness to out-of-domain data, modality-specific features, and languages other than English. Our investigation focuses on three type of models: LSTM models trained on multiple datasets(Cross-Domain), several fusion LSTM models trained with images and text and evaluated with three state-of-the-art embeddings, BERT ELMo, and GloVe (Cross-Modality), and character-level CNN models trained on multiple languages (Cross-Language). Our analyses reveal a significant drop in performance when testing neural models on out-of-domain data and non-English languages that may be mitigated using diverse training data. We find that with additional image content as input, ELMo embeddings yield significantly fewer errors compared to BERT orGLoVe. Most importantly, this work not only carefully analyzes deception model robustness but also provides a framework of these analyses that can be applied to new models or extended datasets in the future. | ['Svitlana Volkova', 'Dustin Arendt', 'Robin Cosbey', 'Ellyn Ayton', 'Maria Glenski'] | 2021-04-23 | null | https://aclanthology.org/2020.rdsm-1.1 | https://aclanthology.org/2020.rdsm-1.1.pdf | rdsm-coling-2020-12 | ['deception-detection'] | ['miscellaneous'] | [ 9.34457630e-02 -2.85838425e-01 8.10410157e-02 -4.23677176e-01
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f6153877-0c7f-4ed1-a902-fbf788b248b9 | pyramid-dilated-deeper-convlstm-for-video | null | null | http://openaccess.thecvf.com/content_ECCV_2018/html/Hongmei_Song_Pseudo_Pyramid_Deeper_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Hongmei_Song_Pseudo_Pyramid_Deeper_ECCV_2018_paper.pdf | Pyramid Dilated Deeper ConvLSTM for Video Salient Object Detection | This paper proposes a fast video salient object detection model, based on a novel recurrent network architecture, named Pyramid Dilated Bidirectional ConvLSTM (PDB-ConvLSTM). A Pyramid Dilated Convolution (PDC) module is first designed for simultaneously extracting spatial features at multiple scales. These spatial features are then concatenated and fed into an extended Deeper Bidirectional ConvLSTM (DB-ConvLSTM) to learn spatiotemporal information. Forward and backward ConvLSTM units are placed in two layers and connected in a cascaded way, encouraging information flow between the bi-directional streams and leading to deeper feature extraction. We further augment DB-ConvLSTM with a PDC-like structure, by adopting several dilated DB-ConvLSTMs to extract multi-scale spatiotemporal information. Extensive experimental results show that our method outperforms previous video saliency models in a large margin, with a real-time speed of 20 fps on a single GPU. With unsupervised video object segmentation as an example application, the proposed model (with a CRF-based post-process) achieves state-of-the-art results on two popular benchmarks, well demonstrating its superior performance and high applicability. | ['Kin-Man Lam', 'Jianbing Shen', 'Wenguan Wang', 'Sanyuan Zhao', 'Hongmei Song'] | 2018-09-01 | null | null | null | eccv-2018-9 | ['video-salient-object-detection', 'unsupervised-video-object-segmentation'] | ['computer-vision', 'computer-vision'] | [ 3.79493982e-01 -1.33437425e-01 -3.60691905e-01 -2.62529135e-01
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1cd54ce4-2abf-4ced-abcd-0891d9db8a5d | deepdistance-a-multi-task-deep-regression | 1908.11211 | null | https://arxiv.org/abs/1908.11211v1 | https://arxiv.org/pdf/1908.11211v1.pdf | DeepDistance: A Multi-task Deep Regression Model for Cell Detection in Inverted Microscopy Images | This paper presents a new deep regression model, which we call DeepDistance, for cell detection in images acquired with inverted microscopy. This model considers cell detection as a task of finding most probable locations that suggest cell centers in an image. It represents this main task with a regression task of learning an inner distance metric. However, different than the previously reported regression based methods, the DeepDistance model proposes to approach its learning as a multi-task regression problem where multiple tasks are learned by using shared feature representations. To this end, it defines a secondary metric, normalized outer distance, to represent a different aspect of the problem and proposes to define its learning as complementary to the main cell detection task. In order to learn these two complementary tasks more effectively, the DeepDistance model designs a fully convolutional network (FCN) with a shared encoder path and end-to-end trains this FCN to concurrently learn the tasks in parallel. DeepDistance uses the inner distances estimated by this FCN in a detection algorithm to locate individual cells in a given image. For further performance improvement on the main task, this paper also presents an extended version of the DeepDistance model. This extended model includes an auxiliary classification task and learns it in parallel to the two regression tasks by sharing feature representations with them. Our experiments on three different human cell lines reveal that the proposed multi-task learning models, the DeepDistance model and its extended version, successfully identify cell locations, even for the cell line that was not used in training, and improve the results of the previous deep learning methods. | ['Cigdem Gunduz-Demir', 'Rengul Cetin-Atalay', 'Gozde Nur Gunesli', 'Can Fahrettin Koyuncu'] | 2019-08-29 | null | null | null | null | ['cell-detection'] | ['computer-vision'] | [ 1.19668774e-01 4.23536673e-02 1.98151752e-01 -7.45715871e-02
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6b2f1dc4-d80f-4b04-8907-9c4039dee3ca | keystroke-dynamics-as-signal-for-shallow | 1610.03321 | null | http://arxiv.org/abs/1610.03321v1 | http://arxiv.org/pdf/1610.03321v1.pdf | Keystroke dynamics as signal for shallow syntactic parsing | Keystroke dynamics have been extensively used in psycholinguistic and writing
research to gain insights into cognitive processing. But do keystroke logs
contain actual signal that can be used to learn better natural language
processing models?
We postulate that keystroke dynamics contain information about syntactic
structure that can inform shallow syntactic parsing. To test this hypothesis,
we explore labels derived from keystroke logs as auxiliary task in a multi-task
bidirectional Long Short-Term Memory (bi-LSTM). Our results show promising
results on two shallow syntactic parsing tasks, chunking and CCG supertagging.
Our model is simple, has the advantage that data can come from distinct
sources, and produces models that are significantly better than models trained
on the text annotations alone. | ['Barbara Plank'] | 2016-10-11 | keystroke-dynamics-as-signal-for-shallow-2 | https://aclanthology.org/C16-1059 | https://aclanthology.org/C16-1059.pdf | coling-2016-12 | ['ccg-supertagging'] | ['natural-language-processing'] | [-2.27468967e-01 1.38880372e-01 -4.23270732e-01 -6.75295234e-01
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9447fae0-f442-4f6c-9a32-a2ea158b54c8 | ocid-ref-a-3d-robotic-dataset-with-embodied | 2103.07679 | null | https://arxiv.org/abs/2103.07679v2 | https://arxiv.org/pdf/2103.07679v2.pdf | OCID-Ref: A 3D Robotic Dataset with Embodied Language for Clutter Scene Grounding | To effectively apply robots in working environments and assist humans, it is essential to develop and evaluate how visual grounding (VG) can affect machine performance on occluded objects. However, current VG works are limited in working environments, such as offices and warehouses, where objects are usually occluded due to space utilization issues. In our work, we propose a novel OCID-Ref dataset featuring a referring expression segmentation task with referring expressions of occluded objects. OCID-Ref consists of 305,694 referring expressions from 2,300 scenes with providing RGB image and point cloud inputs. To resolve challenging occlusion issues, we argue that it's crucial to take advantage of both 2D and 3D signals to resolve challenging occlusion issues. Our experimental results demonstrate the effectiveness of aggregating 2D and 3D signals but referring to occluded objects still remains challenging for the modern visual grounding systems. OCID-Ref is publicly available at https://github.com/lluma/OCID-Ref | ['Wen-Chin Chen', 'Winston H. Hsu', 'Yu-Siang Wang', 'Jen-Wei Wang', 'Hung-Ting Su', 'Yun-Hsuan Liu', 'Ke-Jyun Wang'] | 2021-03-13 | null | https://aclanthology.org/2021.naacl-main.419 | https://aclanthology.org/2021.naacl-main.419.pdf | naacl-2021-4 | ['referring-expression-segmentation'] | ['computer-vision'] | [ 5.75628690e-02 1.20007575e-01 1.23609625e-01 -4.44078028e-01
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0e0ef93e-3272-41cb-b1da-264acfb1f40a | pay-attention-accuracy-versus | 2303.17908 | null | https://arxiv.org/abs/2303.17908v1 | https://arxiv.org/pdf/2303.17908v1.pdf | Pay Attention: Accuracy Versus Interpretability Trade-off in Fine-tuned Diffusion Models | The recent progress of diffusion models in terms of image quality has led to a major shift in research related to generative models. Current approaches often fine-tune pre-trained foundation models using domain-specific text-to-image pairs. This approach is straightforward for X-ray image generation due to the high availability of radiology reports linked to specific images. However, current approaches hardly ever look at attention layers to verify whether the models understand what they are generating. In this paper, we discover an important trade-off between image fidelity and interpretability in generative diffusion models. In particular, we show that fine-tuning text-to-image models with learnable text encoder leads to a lack of interpretability of diffusion models. Finally, we demonstrate the interpretability of diffusion models by showing that keeping the language encoder frozen, enables diffusion models to achieve state-of-the-art phrase grounding performance on certain diseases for a challenging multi-label segmentation task, without any additional training. Code and models will be available at https://github.com/MischaD/chest-distillation. | ['Bernhard Kainz', 'Matthew Baugh', 'Johanna P. Müller', 'Hadrien Reynaud', 'Mischa Dombrowski'] | 2023-03-31 | null | null | null | null | ['phrase-grounding'] | ['natural-language-processing'] | [ 5.79632580e-01 6.11662447e-01 -1.66184023e-01 -6.49249852e-01
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bb9564f4-4261-4d3e-9277-c46d12619206 | a-passage-based-approach-to-learning-to-rank | 1906.02083 | null | https://arxiv.org/abs/1906.02083v1 | https://arxiv.org/pdf/1906.02083v1.pdf | A Passage-Based Approach to Learning to Rank Documents | According to common relevance-judgments regimes, such as TREC's, a document can be deemed relevant to a query even if it contains a very short passage of text with pertinent information. This fact has motivated work on passage-based document retrieval: document ranking methods that induce information from the document's passages. However, the main source of passage-based information utilized was passage-query similarities. We address the challenge of utilizing richer sources of passage-based information to improve document retrieval effectiveness. Specifically, we devise a suite of learning-to-rank-based document retrieval methods that utilize an effective ranking of passages produced in response to the query; the passage ranking is also induced using a learning-to-rank approach. Some of the methods quantify the ranking of the passages of a document. Others utilize the feature-based representation of passages used for learning a passage ranker. Empirical evaluation attests to the clear merits of our methods with respect to highly effective baselines. Our best performing method is based on learning a document ranking function using document-query features and passage-query features of the document's passage most highly ranked. | ['Oren Kurland', 'Eilon Sheetrit', 'Anna Shtok'] | 2019-06-05 | null | null | null | null | ['passage-ranking'] | ['natural-language-processing'] | [ 2.64245152e-01 -6.12779975e-01 -4.95780081e-01 -2.40035713e-01
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b5dca245-497c-48bc-92ae-69ccce05169a | abstractors-transformer-modules-for-symbolic | 2304.00195 | null | https://arxiv.org/abs/2304.00195v2 | https://arxiv.org/pdf/2304.00195v2.pdf | Abstractors: Transformer Modules for Symbolic Message Passing and Relational Reasoning | Reasoning in terms of relations, analogies, and abstraction is a hallmark of human intelligence. An active debate is whether this relies on the use of symbolic processing or can be achieved using the same forms of function approximation that have been used for tasks such as image, audio, and, most recently, language processing. We propose an intermediate approach, motivated by principles of cognitive neuroscience, in which abstract symbols can emerge from distributed, neural representations under the influence of an inductive bias for learning that we refer to as a ``relational bottleneck.'' We present a framework that casts this inductive bias in terms of an extension of Transformers, in which specific types of attention mechanisms enforce the relational bottleneck and transform distributed symbols to implement a form of relational reasoning and abstraction. We theoretically analyze the class of relation functions the models can compute and empirically demonstrate superior sample-efficiency on relational tasks compared to standard Transformer architectures. | ['John Lafferty', 'Jonathan Cohen', 'Taylor Webb', 'Awni Altabaa'] | 2023-04-01 | null | null | null | null | ['relational-reasoning'] | ['natural-language-processing'] | [ 3.60150725e-01 6.14734471e-01 1.27864063e-01 -4.32603896e-01
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f5fc71b4-7dd9-4753-9160-c30c20c9e553 | ssl-2-self-supervised-learning-meets-semi | 2303.05026 | null | https://arxiv.org/abs/2303.05026v1 | https://arxiv.org/pdf/2303.05026v1.pdf | SSL^2: Self-Supervised Learning meets Semi-Supervised Learning: Multiple Sclerosis Segmentation in 7T-MRI from large-scale 3T-MRI | Automated segmentation of multiple sclerosis (MS) lesions from MRI scans is important to quantify disease progression. In recent years, convolutional neural networks (CNNs) have shown top performance for this task when a large amount of labeled data is available. However, the accuracy of CNNs suffers when dealing with few and/or sparsely labeled datasets. A potential solution is to leverage the information available in large public datasets in conjunction with a target dataset which only has limited labeled data. In this paper, we propose a training framework, SSL2 (self-supervised-semi-supervised), for multi-modality MS lesion segmentation with limited supervision. We adopt self-supervised learning to leverage the knowledge from large public 3T datasets to tackle the limitations of a small 7T target dataset. To leverage the information from unlabeled 7T data, we also evaluate state-of-the-art semi-supervised methods for other limited annotation settings, such as small labeled training size and sparse annotations. We use the shifted-window (Swin) transformer1 as our backbone network. The effectiveness of self-supervised and semi-supervised training strategies is evaluated in our in-house 7T MRI dataset. The results indicate that each strategy improves lesion segmentation for both limited training data size and for sparse labeling scenarios. The combined overall framework further improves the performance substantially compared to either of its components alone. Our proposed framework thus provides a promising solution for future data/label-hungry 7T MS studies. | ['Ipek Oguz', 'Francesca Bagnato', 'Kelsey Barter', 'Keejin Yoon', 'Daiwei Lu', 'Dewei Hu', 'Han Liu', 'Hao Li', 'Jiacheng Wang'] | 2023-03-09 | null | null | null | null | ['lesion-segmentation'] | ['medical'] | [ 5.71692169e-01 -7.47439126e-03 -6.82656348e-01 -6.70063138e-01
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89683275-99d3-4747-bcc6-34e868a40ad4 | contextual-affinity-distillation-for-image | 2307.03101 | null | https://arxiv.org/abs/2307.03101v1 | https://arxiv.org/pdf/2307.03101v1.pdf | Contextual Affinity Distillation for Image Anomaly Detection | Previous works on unsupervised industrial anomaly detection mainly focus on local structural anomalies such as cracks and color contamination. While achieving significantly high detection performance on this kind of anomaly, they are faced with logical anomalies that violate the long-range dependencies such as a normal object placed in the wrong position. In this paper, based on previous knowledge distillation works, we propose to use two students (local and global) to better mimic the teacher's behavior. The local student, which is used in previous studies mainly focuses on structural anomaly detection while the global student pays attention to logical anomalies. To further encourage the global student's learning to capture long-range dependencies, we design the global context condensing block (GCCB) and propose a contextual affinity loss for the student training and anomaly scoring. Experimental results show the proposed method doesn't need cumbersome training techniques and achieves a new state-of-the-art performance on the MVTec LOCO AD dataset. | ['Takayuki Okatani', 'Masanori Suganuma', 'Jie Zhang'] | 2023-07-06 | null | null | null | null | ['anomaly-detection'] | ['methodology'] | [ 1.54943705e-01 -8.07164982e-02 9.17703807e-02 -3.46099496e-01
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b6dfff36-8580-47bf-afb3-82cb05404f40 | sequential-place-learning-heuristic-free-high | 2103.02074 | null | https://arxiv.org/abs/2103.02074v1 | https://arxiv.org/pdf/2103.02074v1.pdf | Sequential Place Learning: Heuristic-Free High-Performance Long-Term Place Recognition | Sequential matching using hand-crafted heuristics has been standard practice in route-based place recognition for enhancing pairwise similarity results for nearly a decade. However, precision-recall performance of these algorithms dramatically degrades when searching on short temporal window (TW) lengths, while demanding high compute and storage costs on large robotic datasets for autonomous navigation research. Here, influenced by biological systems that robustly navigate spacetime scales even without vision, we develop a joint visual and positional representation learning technique, via a sequential process, and design a learning-based CNN+LSTM architecture, trainable via backpropagation through time, for viewpoint- and appearance-invariant place recognition. Our approach, Sequential Place Learning (SPL), is based on a CNN function that visually encodes an environment from a single traversal, thus reducing storage capacity, while an LSTM temporally fuses each visual embedding with corresponding positional data -- obtained from any source of motion estimation -- for direct sequential inference. Contrary to classical two-stage pipelines, e.g., match-then-temporally-filter, our network directly eliminates false-positive rates while jointly learning sequence matching from a single monocular image sequence, even using short TWs. Hence, we demonstrate that our model outperforms 15 classical methods while setting new state-of-the-art performance standards on 4 challenging benchmark datasets, where one of them can be considered solved with recall rates of 100% at 100% precision, correctly matching all places under extreme sunlight-darkness changes. In addition, we show that SPL can be up to 70x faster to deploy than classical methods on a 729 km route comprising 35,768 consecutive frames. Extensive experiments demonstrate the... Baseline code available at https://github.com/mchancan/deepseqslam | ['Michael Milford', 'Marvin Chancán'] | 2021-03-02 | null | null | null | null | ['sequential-image-classification', 'sequential-place-learning', 'sequential-place-recognition'] | ['computer-vision', 'robots', 'robots'] | [ 3.08370143e-01 -3.93735647e-01 -1.17384218e-01 -1.84127614e-01
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0d24a501-a990-42eb-b89d-00e0b5d85c25 | dynamic-appearance-a-video-representation-for | 2211.12748 | null | https://arxiv.org/abs/2211.12748v2 | https://arxiv.org/pdf/2211.12748v2.pdf | Dynamic Appearance: A Video Representation for Action Recognition with Joint Training | Static appearance of video may impede the ability of a deep neural network to learn motion-relevant features in video action recognition. In this paper, we introduce a new concept, Dynamic Appearance (DA), summarizing the appearance information relating to movement in a video while filtering out the static information considered unrelated to motion. We consider distilling the dynamic appearance from raw video data as a means of efficient video understanding. To this end, we propose the Pixel-Wise Temporal Projection (PWTP), which projects the static appearance of a video into a subspace within its original vector space, while the dynamic appearance is encoded in the projection residual describing a special motion pattern. Moreover, we integrate the PWTP module with a CNN or Transformer into an end-to-end training framework, which is optimized by utilizing multi-objective optimization algorithms. We provide extensive experimental results on four action recognition benchmarks: Kinetics400, Something-Something V1, UCF101 and HMDB51. | ['Adrian G. Bors', 'Guoxi Huang'] | 2022-11-23 | null | null | null | null | ['video-understanding'] | ['computer-vision'] | [ 3.96540493e-01 -2.86536843e-01 -2.19283819e-01 -2.98289686e-01
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8691fd2e-06a3-4cab-aa11-c3263ae6ac3b | how-useful-are-gradients-for-ood-detection | 2205.10439 | null | https://arxiv.org/abs/2205.10439v1 | https://arxiv.org/pdf/2205.10439v1.pdf | How Useful are Gradients for OOD Detection Really? | One critical challenge in deploying highly performant machine learning models in real-life applications is out of distribution (OOD) detection. Given a predictive model which is accurate on in distribution (ID) data, an OOD detection system will further equip the model with the option to defer prediction when the input is novel and the model has little confidence in prediction. There has been some recent interest in utilizing the gradient information in pre-trained models for OOD detection. While these methods have shown competitive performance, there are misconceptions about the true mechanism underlying them, which conflate their performance with the necessity of gradients. In this work, we provide an in-depth analysis and comparison of gradient based methods and elucidate the key components that warrant their OOD detection performance. We further propose a general, non-gradient based method of OOD detection which improves over previous baselines in both performance and computational efficiency. | ['Jeff Schneider', 'Ian Char', 'Youngseog Chung', 'Conor Igoe'] | 2022-05-20 | null | null | null | null | ['misconceptions'] | ['miscellaneous'] | [ 6.63667023e-02 7.29057863e-02 -5.61357319e-01 -4.12879050e-01
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0be5e562-d7f8-459d-9ebb-ee1d2e21b702 | enterprise-disk-drive-scrubbing-based-on | 2306.17169 | null | https://arxiv.org/abs/2306.17169v1 | https://arxiv.org/pdf/2306.17169v1.pdf | Enterprise Disk Drive Scrubbing Based on Mondrian Conformal Predictors | Disk scrubbing is a process aimed at resolving read errors on disks by reading data from the disk. However, scrubbing the entire storage array at once can adversely impact system performance, particularly during periods of high input/output operations. Additionally, the continuous reading of data from disks when scrubbing can result in wear and tear, especially on larger capacity disks, due to the significant time and energy consumption involved. To address these issues, we propose a selective disk scrubbing method that enhances the overall reliability and power efficiency in data centers. Our method employs a Machine Learning model based on Mondrian Conformal prediction to identify specific disks for scrubbing, by proactively predicting the health status of each disk in the storage pool, forecasting n-days in advance, and using an open-source dataset. For disks predicted as non-healthy, we mark them for replacement without further action. For healthy drives, we create a set and quantify their relative health across the entire storage pool based on the predictor's confidence. This enables us to prioritize selective scrubbing for drives with established scrubbing frequency based on the scrub cycle. The method we propose provides an efficient and dependable solution for managing enterprise disk drives. By scrubbing just 22.7% of the total storage disks, we can achieve optimized energy consumption and reduce the carbon footprint of the data center. | ['Ava Hedayatipour', 'Soundouss Messoudi', 'Jinha Hwang', 'Rahul Vishwakarma'] | 2023-06-01 | null | null | null | null | ['conformal-prediction', 'conformal-prediction'] | ['computer-vision', 'reasoning'] | [-2.04078212e-01 -7.55218565e-02 -5.54990530e-01 1.96815938e-01
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629eb3d8-355a-45f2-a9fd-4fff03b85b19 | semantics-preserved-distortion-for-personal | 2201.00965 | null | https://arxiv.org/abs/2201.00965v2 | https://arxiv.org/pdf/2201.00965v2.pdf | Semantics-Preserved Distortion for Personal Privacy Protection in Information Management | Although machine learning and especially deep learning methods have played an important role in the field of information management, privacy protection is an important and concerning topic in current machine learning models. In information management field, a large number of texts containing personal information are produced by users every day. As the model training on information from users is likely to invade personal privacy, many methods have been proposed to block the learning and memorizing of the sensitive data in raw texts. In this paper, we try to do this more linguistically via distorting the text while preserving the semantics. In practice, we leverage a recently our proposed metric, Neighboring Distribution Divergence, to evaluate the semantic preservation during the distortion. Based on the metric, we propose two frameworks for semantics-preserved distortion, a generative one and a substitutive one. We conduct experiments on named entity recognition, constituency parsing, and machine reading comprehension tasks. Results from our experiments show the plausibility and efficiency of our distortion as a method for personal privacy protection. Moreover, we also evaluate the attribute attack on three privacy-related tasks in the current natural language processing field, and the results show the simplicity and effectiveness of our data-based improvement approach compared to the structural improvement approach. Further, we also investigate the effects of privacy protection in specific medical information management in this work and show that the medical information pre-training model using our approach can effectively reduce the memory of patients and symptoms, which fully demonstrates the practicality of our approach. | ['Xueyi Li', 'Zuchao Li', 'Ping Wang', 'Jiajia Li', 'Hai Zhao', 'Letian Peng'] | 2022-01-04 | null | null | null | null | ['constituency-parsing'] | ['natural-language-processing'] | [ 4.67398405e-01 5.55373549e-01 -9.30137709e-02 -6.45753264e-01
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d02029c5-46ed-468c-bad9-1a0349b13bad | a-theory-of-hypergames-on-graphs-for | 2008.03210 | null | https://arxiv.org/abs/2008.03210v1 | https://arxiv.org/pdf/2008.03210v1.pdf | A Theory of Hypergames on Graphs for Synthesizing Dynamic Cyber Defense with Deception | In this chapter, we present an approach using formal methods to synthesize reactive defense strategy in a cyber network, equipped with a set of decoy systems. We first generalize formal graphical security models--attack graphs--to incorporate defender's countermeasures in a game-theoretic model, called an attack-defend game on graph. This game captures the dynamic interactions between the defender and the attacker and their defense/attack objectives in formal logic. Then, we introduce a class of hypergames to model asymmetric information created by decoys in the attacker-defender interactions. Given qualitative security specifications in formal logic, we show that the solution concepts from hypergames and reactive synthesis in formal methods can be extended to synthesize effective dynamic defense strategy using cyber deception. The strategy takes the advantages of the misperception of the attacker to ensure security specification is satisfied, which may not be satisfiable when the information is symmetric. | ['Jie Fu', 'Abhishek N. Kulkarni'] | 2020-08-07 | null | null | null | null | ['formal-logic'] | ['reasoning'] | [ 1.89853817e-01 7.65568256e-01 6.81030527e-02 3.27922612e-01
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d18b102a-6f7e-4871-94a1-20d46246adcd | revisiting-inlier-and-outlier-specification | 2207.05286 | null | https://arxiv.org/abs/2207.05286v2 | https://arxiv.org/pdf/2207.05286v2.pdf | Know Your Space: Inlier and Outlier Construction for Calibrating Medical OOD Detectors | We focus on the problem of producing well-calibrated out-of-distribution (OOD) detectors, in order to enable safe deployment of medical image classifiers. Motivated by the difficulty of curating suitable calibration datasets, synthetic augmentations have become highly prevalent for inlier/outlier specification. While there have been rapid advances in data augmentation techniques, this paper makes a striking finding that the space in which the inliers and outliers are synthesized, in addition to the type of augmentation, plays a critical role in calibrating OOD detectors. Using the popular energy-based OOD detection framework, we find that the optimal protocol is to synthesize latent-space inliers along with diverse pixel-space outliers. Based on empirical studies with multiple medical imaging benchmarks, we demonstrate that our approach consistently leads to superior OOD detection ($15\% - 35\%$ in AUROC) over the state-of-the-art in a variety of open-set recognition settings. | ['Jayaraman J. Thiagarajan', 'Andreas Spanias', 'Deepta Rajan', 'Rushil Anirudh', 'Yamen Mubarka', 'Vivek Narayanaswamy'] | 2022-07-12 | null | null | null | null | ['open-set-learning'] | ['miscellaneous'] | [ 3.56193930e-01 -1.66851848e-01 -8.00220743e-02 -2.26255059e-01
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f8df13ac-da86-4734-8d8d-efa71a43f61e | interpretability-with-full-complexity-by | 2211.17264 | null | https://arxiv.org/abs/2211.17264v1 | https://arxiv.org/pdf/2211.17264v1.pdf | Interpretability with full complexity by constraining feature information | Interpretability is a pressing issue for machine learning. Common approaches to interpretable machine learning constrain interactions between features of the input, rendering the effects of those features on a model's output comprehensible but at the expense of model complexity. We approach interpretability from a new angle: constrain the information about the features without restricting the complexity of the model. Borrowing from information theory, we use the Distributed Information Bottleneck to find optimal compressions of each feature that maximally preserve information about the output. The learned information allocation, by feature and by feature value, provides rich opportunities for interpretation, particularly in problems with many features and complex feature interactions. The central object of analysis is not a single trained model, but rather a spectrum of models serving as approximations that leverage variable amounts of information about the inputs. Information is allocated to features by their relevance to the output, thereby solving the problem of feature selection by constructing a learned continuum of feature inclusion-to-exclusion. The optimal compression of each feature -- at every stage of approximation -- allows fine-grained inspection of the distinctions among feature values that are most impactful for prediction. We develop a framework for extracting insight from the spectrum of approximate models and demonstrate its utility on a range of tabular datasets. | ['Dani S. Bassett', 'Kieran A. Murphy'] | 2022-11-30 | null | null | null | null | ['interpretable-machine-learning'] | ['methodology'] | [ 4.85683054e-01 5.61305940e-01 -6.13078415e-01 -5.86764276e-01
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9635912d-4bff-4001-b68e-74a7b4eca593 | medcattrainer-a-biomedical-free-text | 1907.07322 | null | https://arxiv.org/abs/1907.07322v1 | https://arxiv.org/pdf/1907.07322v1.pdf | MedCATTrainer: A Biomedical Free Text Annotation Interface with Active Learning and Research Use Case Specific Customisation | We present MedCATTrainer an interface for building, improving and customising a given Named Entity Recognition and Linking (NER+L) model for biomedical domain text. NER+L is often used as a first step in deriving value from clinical text. Collecting labelled data for training models is difficult due to the need for specialist domain knowledge. MedCATTrainer offers an interactive web-interface to inspect and improve recognised entities from an underlying NER+L model via active learning. Secondary use of data for clinical research often has task and context specific criteria. MedCATTrainer provides a further interface to define and collect supervised learning training data for researcher specific use cases. Initial results suggest our approach allows for efficient and accurate collection of research use case specific training data. | ['Daniel Bean', 'Zeljko Kraljevic', 'Thomas Searle', 'Richard Dobson', 'Rebecca Bendayan'] | 2019-07-16 | medcattrainer-a-biomedical-free-text-1 | https://aclanthology.org/D19-3024 | https://aclanthology.org/D19-3024.pdf | ijcnlp-2019-11 | ['text-annotation'] | ['natural-language-processing'] | [ 1.88268006e-01 5.71769059e-01 -4.33690488e-01 -5.83900094e-01
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062a19c7-54ba-479f-839a-277d5d48e369 | transnetr-transformer-based-residual-network | 2303.07428 | null | https://arxiv.org/abs/2303.07428v1 | https://arxiv.org/pdf/2303.07428v1.pdf | TransNetR: Transformer-based Residual Network for Polyp Segmentation with Multi-Center Out-of-Distribution Testing | Colonoscopy is considered the most effective screening test to detect colorectal cancer (CRC) and its precursor lesions, i.e., polyps. However, the procedure experiences high miss rates due to polyp heterogeneity and inter-observer dependency. Hence, several deep learning powered systems have been proposed considering the criticality of polyp detection and segmentation in clinical practices. Despite achieving improved outcomes, the existing automated approaches are inefficient in attaining real-time processing speed. Moreover, they suffer from a significant performance drop when evaluated on inter-patient data, especially those collected from different centers. Therefore, we intend to develop a novel real-time deep learning based architecture, Transformer based Residual network (TransNetR), for colon polyp segmentation and evaluate its diagnostic performance. The proposed architecture, TransNetR, is an encoder-decoder network that consists of a pre-trained ResNet50 as the encoder, three decoder blocks, and an upsampling layer at the end of the network. TransNetR obtains a high dice coefficient of 0.8706 and a mean Intersection over union of 0.8016 and retains a real-time processing speed of 54.60 on the Kvasir-SEG dataset. Apart from this, the major contribution of the work lies in exploring the generalizability of the TransNetR by testing the proposed algorithm on the out-of-distribution (test distribution is unknown and different from training distribution) dataset. As a use case, we tested our proposed algorithm on the PolypGen (6 unique centers) dataset and two other popular polyp segmentation benchmarking datasets. We obtained state-of-the-art performance on all three datasets during out-of-distribution testing. The source code of TransNetR will be made publicly available at https://github.com/DebeshJha. | ['Ulas Bagci', 'Vanshali Sharma', 'Nikhil Kumar Tomar', 'Debesh Jha'] | 2023-03-13 | null | null | null | null | ['polyp-segmentation'] | ['computer-vision'] | [-2.68287417e-02 8.55604634e-02 -1.82727441e-01 -6.91743493e-02
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2411ec58-ff02-4a4e-b7cb-f37cf21fed25 | multidepth-single-image-depth-estimation-via | 1907.11111 | null | https://arxiv.org/abs/1907.11111v1 | https://arxiv.org/pdf/1907.11111v1.pdf | MultiDepth: Single-Image Depth Estimation via Multi-Task Regression and Classification | We introduce MultiDepth, a novel training strategy and convolutional neural network (CNN) architecture that allows approaching single-image depth estimation (SIDE) as a multi-task problem. SIDE is an important part of road scene understanding. It, thus, plays a vital role in advanced driver assistance systems and autonomous vehicles. Best results for the SIDE task so far have been achieved using deep CNNs. However, optimization of regression problems, such as estimating depth, is still a challenging task. For the related tasks of image classification and semantic segmentation, numerous CNN-based methods with robust training behavior have been proposed. Hence, in order to overcome the notorious instability and slow convergence of depth value regression during training, MultiDepth makes use of depth interval classification as an auxiliary task. The auxiliary task can be disabled at test-time to predict continuous depth values using the main regression branch more efficiently. We applied MultiDepth to road scenes and present results on the KITTI depth prediction dataset. In experiments, we were able to show that end-to-end multi-task learning with both, regression and classification, is able to considerably improve training and yield more accurate results. | ['Marco Körner', 'Lukas Liebel'] | 2019-07-25 | null | null | null | null | ['road-scene-understanding'] | ['computer-vision'] | [ 2.49344036e-01 8.67837891e-02 -2.72634089e-01 -6.41005278e-01
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f2ee1c2d-a5d6-4bae-9f53-c62bd96cab72 | poseidon-face-from-depth-for-driver-pose | 1611.10195 | null | http://arxiv.org/abs/1611.10195v3 | http://arxiv.org/pdf/1611.10195v3.pdf | POSEidon: Face-from-Depth for Driver Pose Estimation | Fast and accurate upper-body and head pose estimation is a key task for
automatic monitoring of driver attention, a challenging context characterized
by severe illumination changes, occlusions and extreme poses. In this work, we
present a new deep learning framework for head localization and pose estimation
on depth images. The core of the proposal is a regression neural network,
called POSEidon, which is composed of three independent convolutional nets
followed by a fusion layer, specially conceived for understanding the pose by
depth. In addition, to recover the intrinsic value of face appearance for
understanding head position and orientation, we propose a new Face-from-Depth
approach for learning image faces from depth. Results in face reconstruction
are qualitatively impressive. We test the proposed framework on two public
datasets, namely Biwi Kinect Head Pose and ICT-3DHP, and on Pandora, a new
challenging dataset mainly inspired by the automotive setup. Results show that
our method overcomes all recent state-of-art works, running in real time at
more than 30 frames per second. | ['Roberto Vezzani', 'Guido Borghi', 'Rita Cucchiara', 'Marco Venturelli'] | 2016-11-30 | poseidon-face-from-depth-for-driver-pose-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Borghi_POSEidon_Face-From-Depth_for_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Borghi_POSEidon_Face-From-Depth_for_CVPR_2017_paper.pdf | cvpr-2017-7 | ['head-pose-estimation'] | ['computer-vision'] | [-3.21556211e-01 2.62510240e-01 2.12225094e-01 -9.15102720e-01
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dd655d1b-6019-4a29-9c07-584808c8cb45 | multi-pass-q-networks-for-deep-reinforcement | 1905.04388 | null | https://arxiv.org/abs/1905.04388v1 | https://arxiv.org/pdf/1905.04388v1.pdf | Multi-Pass Q-Networks for Deep Reinforcement Learning with Parameterised Action Spaces | Parameterised actions in reinforcement learning are composed of discrete actions with continuous action-parameters. This provides a framework for solving complex domains that require combining high-level actions with flexible control. The recent P-DQN algorithm extends deep Q-networks to learn over such action spaces. However, it treats all action-parameters as a single joint input to the Q-network, invalidating its theoretical foundations. We analyse the issues with this approach and propose a novel method, multi-pass deep Q-networks, or MP-DQN, to address them. We empirically demonstrate that MP-DQN significantly outperforms P-DQN and other previous algorithms in terms of data efficiency and converged policy performance on the Platform, Robot Soccer Goal, and Half Field Offense domains. | ['George D. Konidaris', 'Steven D. James', 'Craig J. Bester'] | 2019-05-10 | null | null | null | null | ['control-with-prametrised-actions'] | ['playing-games'] | [-1.94595501e-01 1.33465916e-01 -5.72697997e-01 1.02753662e-01
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6bb7d16a-915f-434e-a1a9-f478cf056bc3 | a-fine-to-coarse-convolutional-neural-network | 1805.11790 | null | http://arxiv.org/abs/1805.11790v2 | http://arxiv.org/pdf/1805.11790v2.pdf | A Fine-to-Coarse Convolutional Neural Network for 3D Human Action Recognition | This paper presents a new framework for human action recognition from a 3D
skeleton sequence. Previous studies do not fully utilize the temporal
relationships between video segments in a human action. Some studies
successfully used very deep Convolutional Neural Network (CNN) models but often
suffer from the data insufficiency problem. In this study, we first segment a
skeleton sequence into distinct temporal segments in order to exploit the
correlations between them. The temporal and spatial features of a skeleton
sequence are then extracted simultaneously by utilizing a fine-to-coarse (F2C)
CNN architecture optimized for human skeleton sequences. We evaluate our
proposed method on NTU RGB+D and SBU Kinect Interaction dataset. It achieves
79.6% and 84.6% of accuracies on NTU RGB+D with cross-object and cross-view
protocol, respectively, which are almost identical with the state-of-the-art
performance. In addition, our method significantly improves the accuracy of the
actions in two-person interactions. | ['Nakamasa Inoue', 'Koichi Shinoda', 'Thao Minh Le'] | 2018-05-30 | null | null | null | null | ['3d-human-action-recognition'] | ['computer-vision'] | [ 2.10370690e-01 -4.32543427e-01 -3.23614955e-01 -4.27623093e-01
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abd6cc4f-afba-4777-ad38-c342ff1ba0e4 | designing-optimal-mortality-risk-prediction | 1411.5086 | null | http://arxiv.org/abs/1411.5086v2 | http://arxiv.org/pdf/1411.5086v2.pdf | Designing Optimal Mortality Risk Prediction Scores that Preserve Clinical Knowledge | Many in-hospital mortality risk prediction scores dichotomize predictive
variables to simplify the score calculation. However, hard thresholding in
these additive stepwise scores of the form "add x points if variable v is
above/below threshold t" may lead to critical failures. In this paper, we seek
to develop risk prediction scores that preserve clinical knowledge embedded in
features and structure of the existing additive stepwise scores while
addressing limitations caused by variable dichotomization. To this end, we
propose a novel score structure that relies on a transformation of predictive
variables by means of nonlinear logistic functions facilitating smooth
differentiation between critical and normal values of the variables. We develop
an optimization framework for inferring parameters of the logistic functions
for a given patient population via cyclic block coordinate descent. The
parameters may readily be updated as the patient population and standards of
care evolve. We tested the proposed methodology on two populations: (1) brain
trauma patients admitted to the intensive care unit of the Dell Children's
Medical Center of Central Texas between 2007 and 2012, and (2) adult ICU
patient data from the MIMIC II database. The results are compared with those
obtained by the widely used PRISM III and SOFA scores. The prediction power of
a score is evaluated using area under ROC curve, Youden's index, and
precision-recall balance in a cross-validation study. The results demonstrate
that the new framework enables significant performance improvements over PRISM
III and SOFA in terms of all three criteria. | ['Karla A. Lawson', 'Natalia M. Arzeno', 'Haris Vikalo', 'Sarah V. Duzinski'] | 2014-11-19 | null | null | null | null | ['clinical-knowledge'] | ['miscellaneous'] | [ 1.23566441e-01 -2.90929496e-01 -1.34566113e-01 -4.55583692e-01
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74eecb30-4393-49da-bb79-8c08a899ec3c | rumour-detection-using-graph-neural-network | 2212.10080 | null | https://arxiv.org/abs/2212.10080v1 | https://arxiv.org/pdf/2212.10080v1.pdf | Rumour detection using graph neural network and oversampling in benchmark Twitter dataset | Recently, online social media has become a primary source for new information and misinformation or rumours. In the absence of an automatic rumour detection system the propagation of rumours has increased manifold leading to serious societal damages. In this work, we propose a novel method for building automatic rumour detection system by focusing on oversampling to alleviating the fundamental challenges of class imbalance in rumour detection task. Our oversampling method relies on contextualised data augmentation to generate synthetic samples for underrepresented classes in the dataset. The key idea exploits selection of tweets in a thread for augmentation which can be achieved by introducing a non-random selection criteria to focus the augmentation process on relevant tweets. Furthermore, we propose two graph neural networks(GNN) to model non-linear conversations on a thread. To enhance the tweet representations in our method we employed a custom feature selection technique based on state-of-the-art BERTweet model. Experiments of three publicly available datasets confirm that 1) our GNN models outperform the the current state-of-the-art classifiers by more than 20%(F1-score); 2) our oversampling technique increases the model performance by more than 9%;(F1-score) 3) focusing on relevant tweets for data augmentation via non-random selection criteria can further improve the results; and 4) our method has superior capabilities to detect rumours at very early stage. | ['Preeti Kaur', 'Prince Bansal', 'Shaswat Patel'] | 2022-12-20 | null | null | null | null | ['rumour-detection'] | ['natural-language-processing'] | [ 2.36705080e-01 3.81612867e-01 -8.64490420e-02 -8.62341672e-02
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65c482f2-442a-430e-9c7e-4dd925562cb1 | social-sensor-composition-for-tapestry-scenes | 2003.13684 | null | https://arxiv.org/abs/2003.13684v1 | https://arxiv.org/pdf/2003.13684v1.pdf | Social-Sensor Composition for Tapestry Scenes | The extensive use of social media platforms and overwhelming amounts of imagery data creates unique opportunities for sensing, gathering and sharing information about events. One of its potential applications is to leverage crowdsourced social media images to create a tapestry scene for scene analysis of designated locations and time intervals. The existing attempts however ignore the temporal-semantic relevance and spatio-temporal evolution of the images and direction-oriented scene reconstruction. We propose a novel social-sensor cloud (SocSen) service composition approach to form tapestry scenes for scene analysis. The novelty lies in utilising images and image meta-information to bypass expensive traditional image processing techniques to reconstruct scenes. Metadata, such as geolocation, time and angle of view of an image are modelled as non-functional attributes of a SocSen service. Our major contribution lies on proposing a context and direction-aware spatio-temporal clustering and recommendation approach for selecting a set of temporally and semantically similar services to compose the best available SocSen services. Analytical results based on real datasets are presented to demonstrate the performance of the proposed approach. | ['Tooba Aamir', 'Hai Dong', 'Athman Bouguettaya'] | 2020-03-28 | null | null | null | null | ['service-composition'] | ['miscellaneous'] | [ 3.62484246e-01 -3.22016895e-01 2.92250574e-01 -6.57953024e-01
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7c4b3d0e-17d0-4509-87c6-929e60057874 | robust-domain-adaptation-for-machine-reading | 2209.11615 | null | https://arxiv.org/abs/2209.11615v1 | https://arxiv.org/pdf/2209.11615v1.pdf | Robust Domain Adaptation for Machine Reading Comprehension | Most domain adaptation methods for machine reading comprehension (MRC) use a pre-trained question-answer (QA) construction model to generate pseudo QA pairs for MRC transfer. Such a process will inevitably introduce mismatched pairs (i.e., noisy correspondence) due to i) the unavailable QA pairs in target documents, and ii) the domain shift during applying the QA construction model to the target domain. Undoubtedly, the noisy correspondence will degenerate the performance of MRC, which however is neglected by existing works. To solve such an untouched problem, we propose to construct QA pairs by additionally using the dialogue related to the documents, as well as a new domain adaptation method for MRC. Specifically, we propose Robust Domain Adaptation for Machine Reading Comprehension (RMRC) method which consists of an answer extractor (AE), a question selector (QS), and an MRC model. Specifically, RMRC filters out the irrelevant answers by estimating the correlation to the document via the AE, and extracts the questions by fusing the candidate questions in multiple rounds of dialogue chats via the QS. With the extracted QA pairs, MRC is fine-tuned and provides the feedback to optimize the QS through a novel reinforced self-training method. Thanks to the optimization of the QS, our method will greatly alleviate the noisy correspondence problem caused by the domain shift. To the best of our knowledge, this could be the first study to reveal the influence of noisy correspondence in domain adaptation MRC models and show a feasible way to achieve robustness to mismatched pairs. Extensive experiments on three datasets demonstrate the effectiveness of our method. | ['Xi Peng', 'Zujie Wen', 'Jia Liu', 'Zhenyu Huang', 'Liang Jiang'] | 2022-09-23 | null | null | null | null | ['machine-reading-comprehension'] | ['natural-language-processing'] | [ 5.14535785e-01 3.34680587e-01 4.49968636e-01 -2.44270593e-01
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d9c13ef8-ac2d-45e3-889f-0b8ce8f47209 | androshield-automated-android-applications | null | null | https://www.mdpi.com/2078-2489/10/10/326 | https://www.mdpi.com/2078-2489/10/10/326/pdf | AndroShield: Automated Android Applications Vulnerability Detection, a Hybrid Static and Dynamic Analysis Approach | The security of mobile applications has become a major research field which is associated with a lot of challenges. The high rate of developing mobile applications has resulted in less secure applications. This is due to what is called the “rush to release” as defined by Ponemon Institute. Security testing—which is considered one of the main phases of the development life cycle—is either not performed or given minimal time; hence, there is a need for security testing automation. One of the techniques used is Automated Vulnerability Detection. Vulnerability detection is one of the security tests that aims at pinpointing potential security leaks. Fixing those leaks results in protecting smart-phones and tablet mobile device users against attacks. This paper focuses on building a hybrid approach of static and dynamic analysis for detecting the vulnerabilities of Android applications. This approach is capsuled in a usable platform (web application) to make it easy to use for both public users and professional developers. Static analysis, on one hand, performs code analysis. It does not require running the application to detect vulnerabilities. Dynamic analysis, on the other hand, detects the vulnerabilities that are dependent on the run-time behaviour of the application and cannot be detected using static analysis. The model is evaluated against different applications with different security vulnerabilities. Compared with other detection platforms, our model detects information leaks as well as insecure network requests alongside other commonly detected flaws that harm users’ privacy. The code is available through a GitHub repository for public contribution. | ['Menna Tullah Magdy', 'Nouran Abdeen', 'Hanan Hindy', 'Amr Amin', 'Islam Hegazy', 'Amgad Eldessouki'] | 2019-10-22 | null | null | null | mdpi-information-2019-10 | ['vulnerability-detection', 'mobile-security'] | ['miscellaneous', 'miscellaneous'] | [ 1.05621710e-01 -5.87649830e-03 -1.75239667e-01 2.11458206e-01
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8c1b5e93-1792-43b1-bd9a-78901f747594 | dynamic-video-frame-interpolation-with | 2304.12664 | null | https://arxiv.org/abs/2304.12664v1 | https://arxiv.org/pdf/2304.12664v1.pdf | Dynamic Video Frame Interpolation with integrated Difficulty Pre-Assessment | Video frame interpolation(VFI) has witnessed great progress in recent years. While existing VFI models still struggle to achieve a good trade-off between accuracy and efficiency: fast models often have inferior accuracy; accurate models typically run slowly. However, easy samples with small motion or clear texture can achieve competitive results with simple models and do not require heavy computation. In this paper, we present an integrated pipeline which combines difficulty assessment with video frame interpolation. Specifically, it firstly leverages a pre-assessment model to measure the interpolation difficulty level of input frames, and then dynamically selects an appropriate VFI model to generate interpolation results. Furthermore, a large-scale VFI difficulty assessment dataset is collected and annotated to train our pre-assessment model. Extensive experiments show that easy samples pass through fast models while difficult samples inference with heavy models, and our proposed pipeline can improve the accuracy-efficiency trade-off for VFI. | ['Cheul-hee Hahm', 'Jayoon Koo', 'Jie Chen', 'Longhai Wu', 'Youxin Chen', 'Xin Jin', 'Ban Chen'] | 2023-04-25 | null | null | null | null | ['video-frame-interpolation'] | ['computer-vision'] | [ 8.68004784e-02 -4.57315624e-01 -3.83612931e-01 -4.90858614e-01
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3cba2ca0-3844-4421-adda-28b30cfa7703 | you-talking-to-me-a-corpus-and-algorithm-for | null | null | https://aclanthology.org/P08-1095 | https://aclanthology.org/P08-1095.pdf | You Talking to Me? A Corpus and Algorithm for Conversation Disentanglement | When multiple conversations occur simultaneously, a listener must decide which conversation each utterance is part of in order to interpret and respond to it appropriately. We refer to this task as disentanglement. We present a corpus of Internet Relay Chat (IRC) dialogue in which the various conversations have been manually disentangled, and evaluate annotator reliability. This is, to our knowledge, the first such corpus for internet chat. We propose a graph-theoretic model for disentanglement, using discourse-based features which have not been previously applied to this task. The model’s predicted disentanglements are highly correlated with manual annotations. | ['Eugene Charniak', 'Micha Elsner'] | 2008-06-01 | null | null | null | null | ['conversation-disentanglement'] | ['natural-language-processing'] | [ 4.12903577e-01 7.44141757e-01 1.07405655e-01 -3.38635206e-01
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f92740eb-c05c-4e99-9264-fd744c598772 | transfer-learning-and-local-interpretable | 2211.05633 | null | https://arxiv.org/abs/2211.05633v2 | https://arxiv.org/pdf/2211.05633v2.pdf | Transfer learning and Local interpretable model agnostic based visual approach in Monkeypox Disease Detection and Classification: A Deep Learning insights | The recent development of Monkeypox disease among various nations poses a global pandemic threat when the world is still fighting Coronavirus Disease-2019 (COVID-19). At its dawn, the slow and steady transmission of Monkeypox disease among individuals needs to be addressed seriously. Over the years, Deep learning (DL) based disease prediction has demonstrated true potential by providing early, cheap, and affordable diagnosis facilities. Considering this opportunity, we have conducted two studies where we modified and tested six distinct deep learning models-VGG16, InceptionResNetV2, ResNet50, ResNet101, MobileNetV2, and VGG19-using transfer learning approaches. Our preliminary computational results show that the proposed modified InceptionResNetV2 and MobileNetV2 models perform best by achieving an accuracy ranging from 93% to 99%. Our findings are reinforced by recent academic work that demonstrates improved performance in constructing multiple disease diagnosis models using transfer learning approaches. Lastly, we further explain our model prediction using Local Interpretable Model-Agnostic Explanations (LIME), which play an essential role in identifying important features that characterize the onset of Monkeypox disease. | ['Kishor Datta Gupta', 'Amin G. Alhashim', 'Md Khairul Islam', 'Fatematuj Jahora', 'Md Shahin Ali', 'Tareque Abu Abdullah', 'Md Manjurul Ahsan'] | 2022-11-01 | null | null | null | null | ['disease-prediction'] | ['medical'] | [-3.34150791e-01 -1.13923736e-02 -6.41292810e-01 -2.10112691e-01
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c39570f6-4a0b-4d80-9377-5902aae88555 | xomivae-an-interpretable-deep-learning-model | 2105.12807 | null | https://arxiv.org/abs/2105.12807v2 | https://arxiv.org/pdf/2105.12807v2.pdf | XOmiVAE: an interpretable deep learning model for cancer classification using high-dimensional omics data | The lack of explainability is one of the most prominent disadvantages of deep learning applications in omics. This "black box" problem can undermine the credibility and limit the practical implementation of biomedical deep learning models. Here we present XOmiVAE, a variational autoencoder (VAE) based interpretable deep learning model for cancer classification using high-dimensional omics data. XOmiVAE is capable of revealing the contribution of each gene and latent dimension for each classification prediction, and the correlation between each gene and each latent dimension. It is also demonstrated that XOmiVAE can explain not only the supervised classification but the unsupervised clustering results from the deep learning network. To the best of our knowledge, XOmiVAE is one of the first activation level-based interpretable deep learning models explaining novel clusters generated by VAE. The explainable results generated by XOmiVAE were validated by both the performance of downstream tasks and the biomedical knowledge. In our experiments, XOmiVAE explanations of deep learning based cancer classification and clustering aligned with current domain knowledge including biological annotation and academic literature, which shows great potential for novel biomedical knowledge discovery from deep learning models. | ['Yike Guo', 'Kai Sun', 'XiaoYu Zhang', 'Eloise Withnell'] | 2021-05-26 | null | null | null | null | ['tumour-classification'] | ['medical'] | [-2.28898942e-01 6.92807674e-01 -1.69738755e-01 -3.30940753e-01
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e2ba3b2e-e695-4e05-8a66-ab8cb8aeb6f5 | membership-inference-attack-susceptibility-of | 2104.08305 | null | https://arxiv.org/abs/2104.08305v1 | https://arxiv.org/pdf/2104.08305v1.pdf | Membership Inference Attack Susceptibility of Clinical Language Models | Deep Neural Network (DNN) models have been shown to have high empirical privacy leakages. Clinical language models (CLMs) trained on clinical data have been used to improve performance in biomedical natural language processing tasks. In this work, we investigate the risks of training-data leakage through white-box or black-box access to CLMs. We design and employ membership inference attacks to estimate the empirical privacy leaks for model architectures like BERT and GPT2. We show that membership inference attacks on CLMs lead to non-trivial privacy leakages of up to 7%. Our results show that smaller models have lower empirical privacy leakages than larger ones, and masked LMs have lower leakages than auto-regressive LMs. We further show that differentially private CLMs can have improved model utility on clinical domain while ensuring low empirical privacy leakage. Lastly, we also study the effects of group-level membership inference and disease rarity on CLM privacy leakages. | ['Hong Yu', 'Bhanu Pratap Singh Rawat', 'Abhyuday Jagannatha'] | 2021-04-16 | null | null | null | null | ['membership-inference-attack'] | ['computer-vision'] | [ 1.58665344e-01 6.84056580e-01 -1.92783669e-01 -6.36235595e-01
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7b01be5a-94f9-40b8-9d5e-a33ebdc95293 | generation-of-artificial-facial-drug-abuse | 2304.06106 | null | https://arxiv.org/abs/2304.06106v1 | https://arxiv.org/pdf/2304.06106v1.pdf | Generation of artificial facial drug abuse images using Deep De-identified anonymous Dataset augmentation through Genetics Algorithm (3DG-GA) | In biomedical research and artificial intelligence, access to large, well-balanced, and representative datasets is crucial for developing trustworthy applications that can be used in real-world scenarios. However, obtaining such datasets can be challenging, as they are often restricted to hospitals and specialized facilities. To address this issue, the study proposes to generate highly realistic synthetic faces exhibiting drug abuse traits through augmentation. The proposed method, called "3DG-GA", Deep De-identified anonymous Dataset Generation, uses Genetics Algorithm as a strategy for synthetic faces generation. The algorithm includes GAN artificial face generation, forgery detection, and face recognition. Initially, a dataset of 120 images of actual facial drug abuse is used. By preserving, the drug traits, the 3DG-GA provides a dataset containing 3000 synthetic facial drug abuse images. The dataset will be open to the scientific community, which can reproduce our results and benefit from the generated datasets while avoiding legal or ethical restrictions. | ['Amine Nait-ali', 'Régis Fournier', 'Lou Laurent', 'Hazem Zein'] | 2023-04-12 | null | null | null | null | ['face-generation'] | ['computer-vision'] | [ 5.29031992e-01 3.94037068e-01 -2.82614343e-02 -3.40141445e-01
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90d4c919-2b27-40b0-89ba-9d5bf4b566b0 | multi-source-domain-adaptation-for-text-1 | 2211.09913 | null | https://arxiv.org/abs/2211.09913v1 | https://arxiv.org/pdf/2211.09913v1.pdf | Multi-source Domain Adaptation for Text-independent Forensic Speaker Recognition | Adapting speaker recognition systems to new environments is a widely-used technique to improve a well-performing model learned from large-scale data towards a task-specific small-scale data scenarios. However, previous studies focus on single domain adaptation, which neglects a more practical scenario where training data are collected from multiple acoustic domains needed in forensic scenarios. Audio analysis for forensic speaker recognition offers unique challenges in model training with multi-domain training data due to location/scenario uncertainty and diversity mismatch between reference and naturalistic field recordings. It is also difficult to directly employ small-scale domain-specific data to train complex neural network architectures due to domain mismatch and performance loss. Fine-tuning is a commonly-used method for adaptation in order to retrain the model with weights initialized from a well-trained model. Alternatively, in this study, three novel adaptation methods based on domain adversarial training, discrepancy minimization, and moment-matching approaches are proposed to further promote adaptation performance across multiple acoustic domains. A comprehensive set of experiments are conducted to demonstrate that: 1) diverse acoustic environments do impact speaker recognition performance, which could advance research in audio forensics, 2) domain adversarial training learns the discriminative features which are also invariant to shifts between domains, 3) discrepancy-minimizing adaptation achieves effective performance simultaneously across multiple acoustic domains, and 4) moment-matching adaptation along with dynamic distribution alignment also significantly promotes speaker recognition performance on each domain, especially for the LENA-field domain with noise compared to all other systems. | ['John H. L. Hansen', 'Zhenyu Wang'] | 2022-11-17 | null | null | null | null | ['speaker-recognition'] | ['speech'] | [ 3.27948928e-01 -5.16650558e-01 2.53341824e-01 -4.35937524e-01
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0a97d4b7-500e-4672-b4a9-002988d177ee | efficient-on-device-session-based | 2209.13422 | null | https://arxiv.org/abs/2209.13422v4 | https://arxiv.org/pdf/2209.13422v4.pdf | Efficient On-Device Session-Based Recommendation | On-device session-based recommendation systems have been achieving increasing attention on account of the low energy/resource consumption and privacy protection while providing promising recommendation performance. To fit the powerful neural session-based recommendation models in resource-constrained mobile devices, tensor-train decomposition and its variants have been widely applied to reduce memory footprint by decomposing the embedding table into smaller tensors, showing great potential in compressing recommendation models. However, these model compression techniques significantly increase the local inference time due to the complex process of generating index lists and a series of tensor multiplications to form item embeddings, and the resultant on-device recommender fails to provide real-time response and recommendation. To improve the online recommendation efficiency, we propose to learn compositional encoding-based compact item representations. Specifically, each item is represented by a compositional code that consists of several codewords, and we learn embedding vectors to represent each codeword instead of each item. Then the composition of the codeword embedding vectors from different embedding matrices (i.e., codebooks) forms the item embedding. Since the size of codebooks can be extremely small, the recommender model is thus able to fit in resource-constrained devices and meanwhile can save the codebooks for fast local inference.Besides, to prevent the loss of model capacity caused by compression, we propose a bidirectional self-supervised knowledge distillation framework. Extensive experimental results on two benchmark datasets demonstrate that compared with existing methods, the proposed on-device recommender not only achieves an 8x inference speedup with a large compression ratio but also shows superior recommendation performance. | ['Hongzhi Yin', 'Quoc Viet Hung Nguyen', 'Chaoqun Yang', 'Qinyong Wang', 'Junliang Yu', 'Xin Xia'] | 2022-09-27 | null | null | null | null | ['session-based-recommendations'] | ['miscellaneous'] | [-2.42634676e-02 -4.78786469e-01 -6.28033459e-01 -2.33230010e-01
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66eb6611-8ee9-4b1d-9cfc-6e5f9216bff5 | what-is-the-reward-for-handwriting | 2009.10962 | null | https://arxiv.org/abs/2009.10962v1 | https://arxiv.org/pdf/2009.10962v1.pdf | What is the Reward for Handwriting? -- Handwriting Generation by Imitation Learning | Analyzing the handwriting generation process is an important issue and has been tackled by various generation models, such as kinematics based models and stochastic models. In this study, we use a reinforcement learning (RL) framework to realize handwriting generation with the careful future planning ability. In fact, the handwriting process of human beings is also supported by their future planning ability; for example, the ability is necessary to generate a closed trajectory like '0' because any shortsighted model, such as a Markovian model, cannot generate it. For the algorithm, we employ generative adversarial imitation learning (GAIL). Typical RL algorithms require the manual definition of the reward function, which is very crucial to control the generation process. In contrast, GAIL trains the reward function along with the other modules of the framework. In other words, through GAIL, we can understand the reward of the handwriting generation process from handwriting examples. Our experimental results qualitatively and quantitatively show that the learned reward catches the trends in handwriting generation and thus GAIL is well suited for the acquisition of handwriting behavior. | ['Seiichi Uchida', 'Keisuke Kanda', 'Brian Kenji Iwana'] | 2020-09-23 | null | null | null | null | ['handwriting-generation'] | ['computer-vision'] | [-1.46969199e-01 3.18007797e-01 -8.26085582e-02 4.74584214e-02
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056de8cb-3360-4245-9455-62df1f74769b | point-of-interest-type-prediction-using-text | 2109.00602 | null | https://arxiv.org/abs/2109.00602v1 | https://arxiv.org/pdf/2109.00602v1.pdf | Point-of-Interest Type Prediction using Text and Images | Point-of-interest (POI) type prediction is the task of inferring the type of a place from where a social media post was shared. Inferring a POI's type is useful for studies in computational social science including sociolinguistics, geosemiotics, and cultural geography, and has applications in geosocial networking technologies such as recommendation and visualization systems. Prior efforts in POI type prediction focus solely on text, without taking visual information into account. However in reality, the variety of modalities, as well as their semiotic relationships with one another, shape communication and interactions in social media. This paper presents a study on POI type prediction using multimodal information from text and images available at posting time. For that purpose, we enrich a currently available data set for POI type prediction with the images that accompany the text messages. Our proposed method extracts relevant information from each modality to effectively capture interactions between text and image achieving a macro F1 of 47.21 across eight categories significantly outperforming the state-of-the-art method for POI type prediction based on text-only methods. Finally, we provide a detailed analysis to shed light on cross-modal interactions and the limitations of our best performing model. | ['Nikolaos Aletras', 'Danae Sánchez Villegas'] | 2021-09-01 | null | https://aclanthology.org/2021.emnlp-main.614 | https://aclanthology.org/2021.emnlp-main.614.pdf | emnlp-2021-11 | ['type-prediction'] | ['computer-code'] | [ 1.21103078e-01 -6.30985498e-02 -2.48671770e-01 -2.37998232e-01
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63380f67-cbed-4394-b1f1-686bb94fd870 | what-do-we-need-to-build-explainable-ai | 1712.09923 | null | http://arxiv.org/abs/1712.09923v1 | http://arxiv.org/pdf/1712.09923v1.pdf | What do we need to build explainable AI systems for the medical domain? | Artificial intelligence (AI) generally and machine learning (ML) specifically
demonstrate impressive practical success in many different application domains,
e.g. in autonomous driving, speech recognition, or recommender systems. Deep
learning approaches, trained on extremely large data sets or using
reinforcement learning methods have even exceeded human performance in visual
tasks, particularly on playing games such as Atari, or mastering the game of
Go. Even in the medical domain there are remarkable results. The central
problem of such models is that they are regarded as black-box models and even
if we understand the underlying mathematical principles, they lack an explicit
declarative knowledge representation, hence have difficulty in generating the
underlying explanatory structures. This calls for systems enabling to make
decisions transparent, understandable and explainable. A huge motivation for
our approach are rising legal and privacy aspects. The new European General
Data Protection Regulation entering into force on May 25th 2018, will make
black-box approaches difficult to use in business. This does not imply a ban on
automatic learning approaches or an obligation to explain everything all the
time, however, there must be a possibility to make the results re-traceable on
demand. In this paper we outline some of our research topics in the context of
the relatively new area of explainable-AI with a focus on the application in
medicine, which is a very special domain. This is due to the fact that medical
professionals are working mostly with distributed heterogeneous and complex
sources of data. In this paper we concentrate on three sources: images, *omics
data and text. We argue that research in explainable-AI would generally help to
facilitate the implementation of AI/ML in the medical domain, and specifically
help to facilitate transparency and trust. | ['Constantinos S. Pattichis', 'Chris Biemann', 'Andreas Holzinger', 'Douglas B. Kell'] | 2017-12-28 | null | null | null | null | ['game-of-go'] | ['playing-games'] | [ 1.22051731e-01 1.01116753e+00 -2.51244575e-01 -4.99459028e-01
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febe80e2-4c06-48bf-b594-298ca48c5f08 | benchmark-for-generic-product-detection-a | 1912.09476 | null | https://arxiv.org/abs/1912.09476v2 | https://arxiv.org/pdf/1912.09476v2.pdf | Benchmark for Generic Product Detection: A Low Data Baseline for Dense Object Detection | Object detection in densely packed scenes is a new area where standard object detectors fail to train well. Dense object detectors like RetinaNet trained on large and dense datasets show great performance. We train a standard object detector on a small, normally packed dataset with data augmentation techniques. This dataset is 265 times smaller than the standard dataset, in terms of number of annotations. This low data baseline achieves satisfactory results (mAP=0.56) at standard IoU of 0.5. We also create a varied benchmark for generic SKU product detection by providing full annotations for multiple public datasets. It can be accessed at https://github.com/ParallelDots/generic-sku-detection-benchmark. We hope that this benchmark helps in building robust detectors that perform reliably across different settings in the wild. | ['Srikrishna Varadarajan', 'Muktabh Mayank Srivastava', 'Sonaal Kant'] | 2019-12-19 | null | null | null | null | ['dense-object-detection'] | ['computer-vision'] | [-2.05709934e-01 -7.25970864e-02 -4.67135687e-04 -2.82722116e-01
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04fcfa67-3213-495a-ae4c-7883fed5c0f9 | chinese-ner-using-lattice-lstm | 1805.02023 | null | http://arxiv.org/abs/1805.02023v4 | http://arxiv.org/pdf/1805.02023v4.pdf | Chinese NER Using Lattice LSTM | We investigate a lattice-structured LSTM model for Chinese NER, which encodes
a sequence of input characters as well as all potential words that match a
lexicon. Compared with character-based methods, our model explicitly leverages
word and word sequence information. Compared with word-based methods, lattice
LSTM does not suffer from segmentation errors. Gated recurrent cells allow our
model to choose the most relevant characters and words from a sentence for
better NER results. Experiments on various datasets show that lattice LSTM
outperforms both word-based and character-based LSTM baselines, achieving the
best results. | ['Yue Zhang', 'Jie Yang'] | 2018-05-05 | chinese-ner-using-lattice-lstm-1 | https://aclanthology.org/P18-1144 | https://aclanthology.org/P18-1144.pdf | acl-2018-7 | ['chinese-named-entity-recognition'] | ['natural-language-processing'] | [ 1.36563433e-02 -2.61562705e-01 -2.40973786e-01 -1.96499392e-01
-1.03702986e+00 -6.91064417e-01 8.39799047e-02 3.47787887e-01
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021d0b29-550c-4444-810d-8af0dd2dc0ec | window-detection-in-facade-imagery-a-deep | 2107.10006 | null | https://arxiv.org/abs/2107.10006v1 | https://arxiv.org/pdf/2107.10006v1.pdf | Window Detection In Facade Imagery: A Deep Learning Approach Using Mask R-CNN | The parsing of windows in building facades is a long-desired but challenging task in computer vision. It is crucial to urban analysis, semantic reconstruction, lifecycle analysis, digital twins, and scene parsing amongst other building-related tasks that require high-quality semantic data. This article investigates the usage of the mask R-CNN framework to be used for window detection of facade imagery input. We utilize transfer learning to train our proposed method on COCO weights with our own collected dataset of street view images of facades to produce instance segmentations of our new window class. Experimental results show that our suggested approach with a relatively small dataset trains the network only with transfer learning and augmentation achieves results on par with prior state-of-the-art window detection approaches, even without post-optimization techniques. | ['Mola Ayenew', 'Nils Nordmark'] | 2021-07-21 | null | null | null | null | ['scene-parsing'] | ['computer-vision'] | [ 5.96624732e-01 6.65103048e-02 2.95335680e-01 -5.96306264e-01
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df3db08c-f02a-413b-beb6-afa260479830 | fast-optimal-transport-through-sliced | 2307.01770 | null | https://arxiv.org/abs/2307.01770v1 | https://arxiv.org/pdf/2307.01770v1.pdf | Fast Optimal Transport through Sliced Wasserstein Generalized Geodesics | Wasserstein distance (WD) and the associated optimal transport plan have been proven useful in many applications where probability measures are at stake. In this paper, we propose a new proxy of the squared WD, coined min-SWGG, that is based on the transport map induced by an optimal one-dimensional projection of the two input distributions. We draw connections between min-SWGG and Wasserstein generalized geodesics in which the pivot measure is supported on a line. We notably provide a new closed form for the exact Wasserstein distance in the particular case of one of the distributions supported on a line allowing us to derive a fast computational scheme that is amenable to gradient descent optimization. We show that min-SWGG is an upper bound of WD and that it has a complexity similar to as Sliced-Wasserstein, with the additional feature of providing an associated transport plan. We also investigate some theoretical properties such as metricity, weak convergence, computational and topological properties. Empirical evidences support the benefits of min-SWGG in various contexts, from gradient flows, shape matching and image colorization, among others. | ['Nicolas Courty', 'Clément Bonet', 'Gilles Gasso', 'Laetitia Chapel', 'Guillaume Mahey'] | 2023-07-04 | null | null | null | null | ['colorization'] | ['computer-vision'] | [-1.44001648e-01 1.27814427e-01 1.63949475e-01 -2.90512532e-01
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eb149e0c-6de3-4804-a4d1-936b53c012d7 | stacked-convolutional-deep-encoding-network | 2004.04959 | null | https://arxiv.org/abs/2004.04959v1 | https://arxiv.org/pdf/2004.04959v1.pdf | Stacked Convolutional Deep Encoding Network for Video-Text Retrieval | Existing dominant approaches for cross-modal video-text retrieval task are to learn a joint embedding space to measure the cross-modal similarity. However, these methods rarely explore long-range dependency inside video frames or textual words leading to insufficient textual and visual details. In this paper, we propose a stacked convolutional deep encoding network for video-text retrieval task, which considers to simultaneously encode long-range and short-range dependency in the videos and texts. Specifically, a multi-scale dilated convolutional (MSDC) block within our approach is able to encode short-range temporal cues between video frames or text words by adopting different scales of kernel size and dilation size of convolutional layer. A stacked structure is designed to expand the receptive fields by repeatedly adopting the MSDC block, which further captures the long-range relations between these cues. Moreover, to obtain more robust textual representations, we fully utilize the powerful language model named Transformer in two stages: pretraining phrase and fine-tuning phrase. Extensive experiments on two different benchmark datasets (MSR-VTT, MSVD) show that our proposed method outperforms other state-of-the-art approaches. | ['Zheng-Jun Zha', 'Rui Zhao', 'Kecheng Zheng'] | 2020-04-10 | null | null | null | null | ['video-text-retrieval'] | ['computer-vision'] | [-1.21126086e-01 -8.90111148e-01 -2.30718330e-01 -3.61321092e-01
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eaa14728-7cfd-462b-a0c1-1e5d197bbdba | the-story-of-qos-prediction-in-vehicular | 2302.11966 | null | https://arxiv.org/abs/2302.11966v1 | https://arxiv.org/pdf/2302.11966v1.pdf | The Story of QoS Prediction in Vehicular Communication: From Radio Environment Statistics to Network-Access Throughput Prediction | As cellular networks evolve towards the 6th Generation (6G), Machine Learning (ML) is seen as a key enabling technology to improve the capabilities of the network. ML provides a methodology for predictive systems, which, in turn, can make networks become proactive. This proactive behavior of the network can be leveraged to sustain, for example, a specific Quality of Service (QoS) requirement. With predictive Quality of Service (pQoS), a wide variety of new use cases, both safety- and entertainment-related, are emerging, especially in the automotive sector. Therefore, in this work, we consider maximum throughput prediction enhancing, for example, streaming or HD mapping applications. We discuss the entire ML workflow highlighting less regarded aspects such as the detailed sampling procedures, the in-depth analysis of the dataset characteristics, the effects of splits in the provided results, and the data availability. Reliable ML models need to face a lot of challenges during their lifecycle. We highlight how confidence can be built on ML technologies by better understanding the underlying characteristics of the collected data. We discuss feature engineering and the effects of different splits for the training processes, showcasing that random splits might overestimate performance by more than twofold. Moreover, we investigate diverse sets of input features, where network information proved to be most effective, cutting the error by half. Part of our contribution is the validation of multiple ML models within diverse scenarios. We also use Explainable AI (XAI) to show that ML can learn underlying principles of wireless networks without being explicitly programmed. Our data is collected from a deployed network that was under full control of the measurement team and covered different vehicular scenarios and radio environments. | ['Slawomir Stanczak', 'Hans D. Schotten', 'Frank H. P. Fitzek', 'Gerhard Fettweis', 'Martin Kasparick', 'Raja Sattiraju', 'Anton Krause', 'Philipp Geuer', 'Sanket Partani', 'Rodrigo Hernangomez', 'Cara Watermann', 'Daniel F. Külzer', 'Christian L. Vielhaus', 'Alexandros Palaios'] | 2023-02-23 | null | null | null | null | ['feature-engineering'] | ['methodology'] | [-3.05883656e-03 3.30277979e-01 -4.62903172e-01 -3.46883923e-01
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bbc482f9-0315-43d6-b33b-432b6f683e0c | analysis-of-word-embeddings-and-sequence | null | null | https://aclanthology.org/U15-1003 | https://aclanthology.org/U15-1003.pdf | Analysis of Word Embeddings and Sequence Features for Clinical Information Extraction | null | ['Laurianne Sitbon', 'Mahnoosh Kholghi', 'Lance De Vine', 'Guido Zuccon', 'Anthony Nguyen'] | 2015-12-01 | analysis-of-word-embeddings-and-sequence-1 | https://aclanthology.org/U15-1003 | https://aclanthology.org/U15-1003.pdf | alta-2015-12 | ['clinical-concept-extraction'] | ['medical'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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