Depression can significantly impact many aspects of an individual’s life, including their personal and social functioning, academic and work performance, and overall quality of life. Many researchers within the field...
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We propose a reparametrization scheme to address the challenges of applying differentially private SGD on large neural networks, which are 1) the huge memory cost of storing individual gradients, 2) the added noise su...
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In recent years,e-sports has rapidly developed,and the industry has produced large amounts of data with specifications,and these data are easily to be *** to the above characteristics,data mining and deep learning met...
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In recent years,e-sports has rapidly developed,and the industry has produced large amounts of data with specifications,and these data are easily to be *** to the above characteristics,data mining and deep learning methods can be used to guide players and develop appropriate strategies to win *** one of the world’s most famous e-sports events,Dota2 has a large audience base and a good game system.A victory in a game is often associated with a hero’s match,and players are often unable to pick the best lineup to *** solve this problem,in this paper,we present an improved bidirectional Long Short-Term Memory(LSTM)neural network model for Dota2 lineup *** model uses the Continuous Bag Of Words(CBOW)model in the Word2 vec model to generate hero *** CBOW model can predict the context of a word in a ***,a word is transformed into a hero,a sentence into a lineup,and a word vector into a hero vector,the model applied in this article recommends the last hero according to the first four heroes selected first,thereby solving a series of recommendation problems.
Epithelial and stromal tissues are components of the tumor microenvironment and play a major role in tumor initiation and *** stroma from epithelial tissues is critically important for spatial characterization of the ...
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Epithelial and stromal tissues are components of the tumor microenvironment and play a major role in tumor initiation and *** stroma from epithelial tissues is critically important for spatial characterization of the tumor ***,we propose BrcaSeg,an imageanalysis pipeline based on a convolutional neural network(CNN)model to classify epithelial and stromal regions in whole-slide hematoxylin and eosin(H&E)stained histopathological *** CNN model is trained using well-annotated breast cancer tissue microarrays and validated with images from The Cancer Genome Atlas(TCGA)*** achieves a classification accuracy of 91.02%,which outperforms other state-of-the-art *** this model,we generate pixel-level epithelial/stromal tissue maps for 1000 TCGA breast cancer slide images that are paired with gene expression *** subsequently estimate the epithelial and stromal ratios and perform correlation analysis to model the relationship between gene expression and tissue *** Ontology(GO)enrichment analyses of genes that are highly correlated with tissue ratios suggest that the same tissue is associated with similar biological processes in different breast cancer subtypes,whereas each subtype also has its own idiosyncratic biological processes governing the development of these *** all together,our approach can lead to new insights in exploring relationships between image-based phenotypes and their underlying genomic events and biological processes for all types of solid *** can be accessed at https://***/Serian1992/ImgBio.
Network embedding has recently emerged as a promising technique to embed nodes of a network into low-dimensional vectors. While fairly successful, most existing works focus on the embedding techniques for static netwo...
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Deepfake detection, the task of automatically discriminating machine-generated text, is increasingly critical with recent advances in natural language generative models. Existing approaches to deepfake detection typic...
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Purpose With the rapid development of computer vision and artificial intelligence technology, visual object detection has made unprecedented progress, and small object detection in complex scenes has attracted more an...
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Facial image based kinship verification aims to decide whether there exists kinship between the given facial images. In practice, the cross-generation differences will cause adverse effects on kinship verification, wh...
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ISBN:
(数字)9798350390155
ISBN:
(纸本)9798350390162
Facial image based kinship verification aims to decide whether there exists kinship between the given facial images. In practice, the cross-generation differences will cause adverse effects on kinship verification, which limits the performance. Therefore, how to mine the implied similarity from facial images with large cross-generation divergence is an important problem in kinship verification, which has not yet been well studied. In view of this, we propose a Similarity Mining via Implicit matching pattern LEarning (SMILE) approach for kinship verification. Specifically, SMILE mainly consists of two modules, including a Semi-coupled Multi-pattern Similarity Learning (SMSL) module and a Cross-Generation Feature Normalization (CGFN) module. The SMSL module is designed to learn multiple semi-coupled matching patterns for mining the implicit facial similarity information from different perspectives. The CGFN module aims to reduce the divergence between facial images of parent and child. Extensive experiments demonstrate that the proposed approach outperforms the existing state-of-the-art methods.
Vision-and-Language Navigation (VLN), as a crucial research problem of Embodied AI, requires an embodied agent to navigate through complex 3D environments following natural language instructions. Recent research has h...
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Vision-and-Language Navigation (VLN), as a crucial research problem of Embodied AI, requires an embodied agent to navigate through complex 3D environments following natural language instructions. Recent research has highlighted the promising capacity of large language models (LLMs) in VLN by improving navigational reasoning accuracy and interpretability. However, their predominant use in an offline manner usually suffers from substantial domain gap between the VLN task and the LLM training corpus. This paper proposes a novel strategy called Navigational Chain-of-Thought (NavCoT), where we fulfill parameter-efficient in-domain training to enable self-guided navigational decision, leading to a significant mitigation of the domain gap in a cost-effective manner. Specifically, at each timestep, the LLM is prompted to forecast the navigational chain-of-thought by: 1) acting as a world model to imagine the next observation according to the instruction, 2) selecting the candidate observation that best aligns with the imagination, and 3) determining the action based on the reasoning from the prior steps. In this way, the action prediction can be effectively simplified benefiting from the disentangled reasoning. Through constructing formalized labels for training, the LLM can learn to generate desired and reasonable chain-of-thought outputs for improving the action decision. Experimental results across various training settings and popular VLN benchmarks (e.g., Room-to-Room (R2R), Room-across-Room (RxR), Room-for-Room (R4R)) show the significant superiority of NavCoT over the direct action prediction variants. Through simple parameter-efficient finetuning, our NavCoT outperforms a recent GPT4-based approach with ∼7% relative improvement on the R2R dataset. We believe that NavCoT will help unlock more task-adaptive and scalable LLM-based embodied agents, which are helpful for developing real-world robotics applications. Code is available at https://***/expectorlin/NavC
Many existing anomaly detection methods assume the availability of a large-scale normal dataset. But for many applications, limited by resources, removing all anomalous samples from a large unlabeled dataset is unreal...
Many existing anomaly detection methods assume the availability of a large-scale normal dataset. But for many applications, limited by resources, removing all anomalous samples from a large unlabeled dataset is unrealistic, resulting in contaminated datasets. To detect anomalies accurately under such scenarios, from the probabilistic perspective, the key question becomes how to learn the normal-data distribution from a contaminated dataset. To this end, we propose to collect two additional small datasets that are comprised of partially-observed normal and anomaly samples, and then use them to help learn the distribution under an adversarial learning scheme. We prove that under some mild conditions, the proposed method is able to learn the correct normal-data distribution. Then, we consider the overfitting issue caused by the small size of the two additional datasets, and a correctness-guaranteed flipping mechanism is further developed to alleviate it. Theoretical results under incomplete observed anomaly types are also presented. Extensive experimental results demonstrate that our method outperforms representative baselines when detecting anomalies under contaminated datasets.
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