The current study is defined by two main aims. An effective strategy for improving local search is to combine the Set Algebra-Based Heuristic Algorithm (SAHA) algorithm with the Nelder-Mead simplex method. The approac...
详细信息
We introduce camera ray matching (CRAYM) into the joint optimization of camera poses and neural fields from multi-view images. The optimized field, referred to as a feature volume, can be "probed" by the cam...
Sentiment analysis can be used to identify if a text’s sentiment is neutral, positive, or negative. One type of natural language processing is sentiment analysis. An interdisciplinary field encompassing linguistics, ...
详细信息
Skin cancer poses a significant burden on mankind and healthcare systems globally, necessitating the development of effective diagnostic and treatment strategies. This paper introduces FusionEXNet, an innovative and i...
详细信息
Biosignal representation learning (BRL) plays a crucial role in emotion recognition for game users (ERGU). Unsupervised BRL has garnered attention considering the difficulty in obtaining ground truth emotion labels fr...
详细信息
Biosignal representation learning (BRL) plays a crucial role in emotion recognition for game users (ERGU). Unsupervised BRL has garnered attention considering the difficulty in obtaining ground truth emotion labels from game users. However, unsupervised BRL in ERGU faces challenges, including overfitting caused by limited data and performance degradation due to unbalanced sample distributions. Faced with the above challenges, we propose a novel method of biosignal contrastive representation learning (BCRL) for ERGU, which not only serves as a unified representation learning approach applicable to various modalities of biosignals but also derives generalized biosignals representations suitable for different downstream tasks. Specifically, we solve the overfitting by introducing perturbations at the embedding layer based on the projected gradient descent (PGD) adversarial attacks and develop the sample balancing strategy (SBS) to mitigate the negative impact of the unbalanced sample on the performance. Further, we have conducted comprehensive validation experiments on the public dataset, yielding the following key observations: 1) BCRL outperforms all other methods, achieving average accuracies of 76.67%, 71.83%, and 63.58% in 1D-2C Valence, 1D-2C Arousal and 2D-4C Valence/Arousal, respectively;2) The ablation study shows that both the PGD module (+7.58% in accuracy on average) and the SBS module (+14.60% in accuracy on average) have a positive effect on the performance of different classifications;3) BCRL model exhibits the certain generalization ability across the different games, subjects and classifiers. IEEE
Virtual experiences can significantly influence our perception and behavior in the real world, shaping how we interact with and navigate physical environments. In this paper, we examine the impact of learning navigati...
详细信息
Hate speech detection is a vital task in the context of content moderation, aiming to identify and mitigate harmful language in online platforms. This abstract presents a methodology utilizing Recurrent Neural Network...
详细信息
The telegrapher’s equations constitute a set of linear partial differential equations that establish a mathematical correspondence between the electrical current and voltage within transmission lines, taking into acc...
详细信息
Extracting valuable information frombiomedical texts is one of the current research hotspots of concern to a wide range of *** biomedical corpus contains numerous complex long sentences and overlapping relational trip...
详细信息
Extracting valuable information frombiomedical texts is one of the current research hotspots of concern to a wide range of *** biomedical corpus contains numerous complex long sentences and overlapping relational triples,making most generalized domain joint modeling methods difficult to apply effectively in this *** a complex semantic environment in biomedical texts,in this paper,we propose a novel perspective to perform joint entity and relation extraction;existing studies divide the relation triples into several steps or ***,the three elements in the relation triples are interdependent and inseparable,so we regard joint extraction as a tripartite classification *** the same time,fromthe perspective of triple classification,we design amulti-granularity 2D convolution to refine the word pair table and better utilize the dependencies between biomedical word ***,we use a biaffine predictor to assist in predicting the labels of word pairs for relation *** model(MCTPL)Multi-granularity Convolutional Tokens Pairs of Labeling better utilizes the elements of triples and improves the ability to extract overlapping triples compared to previous ***,we evaluated our model on two publicly accessible *** experimental results show that our model’s ability to extract relation triples on the CPI dataset improves the F1 score by 2.34%compared to the current optimal *** the DDI dataset,the F1 value improves the F1 value by 1.68%compared to the current optimal *** model achieved state-of-the-art performance compared to other baseline models in biomedical text entity relation extraction.
Machine learning techniques have become ubiquitous both in industry and academic *** model sizes and training data volumes necessitate fast and efficient distributed training *** communications greatly simplify inter-...
详细信息
Machine learning techniques have become ubiquitous both in industry and academic *** model sizes and training data volumes necessitate fast and efficient distributed training *** communications greatly simplify inter-and intra-node data transfer and are an essential part of the distributed training process as information such as gradients must be shared between processing *** this paper,we survey the current state-of-the-art collective communication libraries(namely xCCL,including NCCL,oneCCL,RCCL,MSCCL,ACCL,and Gloo),with a focus on the industry-led ones for deep learning *** investigate the design features of these xCCLs,discuss their use cases in the industry deep learning workloads,compare their performance with industry-made benchmarks(i.e.,NCCL Tests and PARAM),and discuss key take-aways and interesting *** believe our survey sheds light on potential research directions of future designs for xCCLs.
暂无评论