標(biāo)題: Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2022; 31st International C Elias Pimenidis,Plamen Angelov,Mehmet Aydin Conference p [打印本頁] 作者: 母牛膽小鬼 時間: 2025-3-21 16:51
書目名稱Artificial Neural Networks and Machine Learning – ICANN 2022影響因子(影響力)
書目名稱Artificial Neural Networks and Machine Learning – ICANN 2022影響因子(影響力)學(xué)科排名
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書目名稱Artificial Neural Networks and Machine Learning – ICANN 2022網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Artificial Neural Networks and Machine Learning – ICANN 2022被引頻次
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書目名稱Artificial Neural Networks and Machine Learning – ICANN 2022讀者反饋
書目名稱Artificial Neural Networks and Machine Learning – ICANN 2022讀者反饋學(xué)科排名
作者: Minatory 時間: 2025-3-21 22:03
https://doi.org/10.1007/978-3-642-91296-2loss in the training phase. This instantiation requires no additional computation cost or customized architectures but only a masking function. Empirical results from various network architectures indicate its feasibility and effectiveness of alleviating overconfident failure predictions in semantic作者: 轉(zhuǎn)換 時間: 2025-3-22 03:19 作者: 使痛苦 時間: 2025-3-22 07:02
Die drei Grenztypen im einzelnen,an-labeled story so as to refine the generation process. Experimental results on the VIST dataset and human evaluation demonstrate that our model outperforms most of the cutting-edge models across multiple evaluation metrics.作者: 刺激 時間: 2025-3-22 12:32 作者: Expiration 時間: 2025-3-22 16:56 作者: 字的誤用 時間: 2025-3-22 21:05
Sukhkamal B. Campbell,Terri L. Woodard the information of all agents and simplify the complex interactions among agents into low-dimensional representations. Pheromones perceived by agents can be regarded as a summary of the views of nearby agents which can better reflect the real situation of the environment. Q-Learning is taken as our作者: CRAFT 時間: 2025-3-23 01:06 作者: 祖先 時間: 2025-3-23 04:27 作者: Commodious 時間: 2025-3-23 08:31
New Insights into Ovarian Functionod called logit replacement, which can adaptively fix teachers’ mistakes to avoid genetic errors. We conducted comprehensive experiments on the basis of the SemEval-2010 Task 8 relation classification benchmark. Test results demonstrate the effectiveness of the proposed methods.作者: osculate 時間: 2025-3-23 11:01 作者: 帶傷害 時間: 2025-3-23 15:44 作者: Common-Migraine 時間: 2025-3-23 20:35
Fertility Control — Update and Trendsxt and multi-sourced electronic health records (EHRs), a challenging task for standard transformers designed to work on short input sequences. A vital contribution of this research is new state-of-the-art (SOTA) results obtained using TransformerXL for predicting medical codes. A variety of experime作者: 好色 時間: 2025-3-23 22:53
https://doi.org/10.1007/978-4-431-55151-5lem of random initialization of parameters in zero-shot settings, we elicit knowledge from pretrained language models to form initial prototypical embeddings. Our method optimizes models by contrastive learning. Extensive experimental results on several many-class text classification datasets with l作者: 生命層 時間: 2025-3-24 04:59
,Alleviating Overconfident Failure Predictions via?Masking Predictive Logits in?Semantic Segmentatioloss in the training phase. This instantiation requires no additional computation cost or customized architectures but only a masking function. Empirical results from various network architectures indicate its feasibility and effectiveness of alleviating overconfident failure predictions in semantic作者: 歌曲 時間: 2025-3-24 08:21
,Cooperative Multi-agent Reinforcement Learning with?Hierachical Communication Architecture,level to communicate efficiently and provide guidance for the low level to coordinate. This hierarchical communication architecture conveys several benefits: 1) It coarsens the collaborative granularity and reduces the requirement of communication since communication happens only in high level at a 作者: 半身雕像 時間: 2025-3-24 13:01 作者: expansive 時間: 2025-3-24 14:57
,Long-Horizon Route-Constrained Policy for?Learning Continuous Control Without Exploration,subgoal constraints. It can constrain the state space and action space of the agent. And it can correct trajectories with temporal information. Experiments on the D4RL benchmark show that our approach achieves higher scores with state-of-the-art methods and enhances performance on complex tasks.作者: patriarch 時間: 2025-3-24 19:13 作者: Condyle 時間: 2025-3-25 00:06
,Pheromone-inspired Communication Framework for?Large-scale Multi-agent Reinforcement Learning, the information of all agents and simplify the complex interactions among agents into low-dimensional representations. Pheromones perceived by agents can be regarded as a summary of the views of nearby agents which can better reflect the real situation of the environment. Q-Learning is taken as our作者: CHOIR 時間: 2025-3-25 05:02 作者: 熱情的我 時間: 2025-3-25 11:05
,A Novel Approach to?Train Diverse Types of?Language Models for?Health Mention Classification of?Twe that adding noise at earlier layers improves models’ performance whereas adding noise at intermediate layers deteriorates models’ performance. Finally, adding noise towards the final layers performs better than the middle layers noise addition.作者: SNEER 時間: 2025-3-25 15:12
,Adaptive Knowledge Distillation for?Efficient Relation Classification,od called logit replacement, which can adaptively fix teachers’ mistakes to avoid genetic errors. We conducted comprehensive experiments on the basis of the SemEval-2010 Task 8 relation classification benchmark. Test results demonstrate the effectiveness of the proposed methods.作者: 字形刻痕 時間: 2025-3-25 18:46
,An Unsupervised Sentence Embedding Method by?Maximizing the?Mutual Information of?Augmented Text Reobal MI maximization as well as supervised ones. In this paper, we propose an unsupervised sentence embedding method by maximizing the mutual information of augmented text representations. Experimental results show that our model achieves an average of 73.36% Spearman’s correlation on a series of se作者: Diatribe 時間: 2025-3-25 23:43
,Chinese Named Entity Recognition Using the?Improved Transformer Encoder and?the?Lexicon Adapter,the position embedding and the self-attention calculation method in the Transformer encoder. Finally, we propose a new architecture of Chinese NER using the improved Transformer encoder and the lexicon adapter. On the four datasets of the Chinese NER task, our model achieves better performance than 作者: 想象 時間: 2025-3-26 02:27
Concatenating BioMed-Transformers to Tackle Long Medical Documents and to Improve the Prediction ofxt and multi-sourced electronic health records (EHRs), a challenging task for standard transformers designed to work on short input sequences. A vital contribution of this research is new state-of-the-art (SOTA) results obtained using TransformerXL for predicting medical codes. A variety of experime作者: unstable-angina 時間: 2025-3-26 04:51
,Eliciting Knowledge from?Pretrained Language Models for?Prototypical Prompt Verbalizer,lem of random initialization of parameters in zero-shot settings, we elicit knowledge from pretrained language models to form initial prototypical embeddings. Our method optimizes models by contrastive learning. Extensive experimental results on several many-class text classification datasets with l作者: 出價 時間: 2025-3-26 08:31
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/b/image/162658.jpg作者: 紅腫 時間: 2025-3-26 14:46 作者: Ancestor 時間: 2025-3-26 17:10
https://doi.org/10.1007/978-3-642-91296-2 can better deal with dynamic environment and thus make optimal decisions. However, restricted by the limited communication channel, agents have to leverage less communication resources to transmit more informative messages. In this article, we propose a two-level hierarchical multi-agent reinforcem作者: 個阿姨勾引你 時間: 2025-3-26 22:26
Die drei Grenztypen im einzelnen,e captioning, the story contains not only factual descriptions but also concepts and objects that do not explicitly appear in the input images. Recent works utilize either end-to-end or multi-stage frameworks to produce more relevant and coherent stories but usually ignore latent emotional informati作者: moratorium 時間: 2025-3-27 04:17 作者: 沒血色 時間: 2025-3-27 05:40 作者: 心神不寧 時間: 2025-3-27 10:32
Die drei Grenztypen im einzelnen,ever, existing researches generally combine a basic RL framework Ape-X DQN with the graph convolutional network (GCN), to aggregate the neighborhood information, lacking unique collaboration exploration at each intersection with shared parameters. This paper proposes a multi-mode Light model that le作者: indecipherable 時間: 2025-3-27 17:27
Sukhkamal B. Campbell,Terri L. Woodard multi-agent systems are hard to extend to large-scale ones because the latter is far more dynamic and the number of interactions increases exponentially with the growing number of agents. Some swarm intelligence algorithms simulate the mechanism of pheromones to control large-scale agent coordinati作者: chondromalacia 時間: 2025-3-27 18:05
Sukhkamal B. Campbell,Terri L. Woodardany heuristic algorithms to solve them. However, with the continuous expansion of logistics scale, these methods generally have the problem of too long calculation time. In order to solve this problem, we propose a reinforcement learning (RL) model based on the Advantage Actor-Critic, which regards 作者: EXULT 時間: 2025-3-28 01:56
Pregnancy After Gynecological Cancer by structured entities and relations. Our proposal takes a hybrid connectionist-symbolic approach, where a classical actor-critic method with an iterative weight update scheme is used to guide the derivation of an agent’s policy, which is purely expressed as first-order logic. A recent technique, d作者: Resection 時間: 2025-3-28 02:38 作者: MURKY 時間: 2025-3-28 07:47 作者: 使厭惡 時間: 2025-3-28 11:40
https://doi.org/10.1007/978-3-642-02062-9ease words adds challenges to the task. Recently, adversarial training acting as a means of regularization has gained popularity in many NLP tasks. In this paper, we propose a novel approach to train language models for health mention classification of tweets that involves adversarial training. We g作者: Alpha-Cells 時間: 2025-3-28 17:12
New Insights into Ovarian Functionowever, there are currently no researchers focusing on KD’s application for relation classification. Although directly leveraging traditional KD methods for relation classification is the easiest way, it should not be neglected that the concept of “relation” is highly ambiguous so machine learning m作者: FER 時間: 2025-3-28 21:14
Progesterone Receptors and Ovulationy of the text. An adversarial multi-task learning method is proposed to enhance the modeling and detection ability of character polysemy in Chinese sentence context. Wherein, two models, the masked language model and scoring language model, are introduced as a pair of not only coupled but also adver作者: vasospasm 時間: 2025-3-29 01:34
Ursula-F. Habenicht,R. John Aitkenanner. However, unsupervised methods pale by comparison to supervised ones on many tasks. Recently, some unsupervised methods propose to learn sentence representations by maximizing the mutual information between text representations of different levels, such as global MI maximization: global and gl作者: 填料 時間: 2025-3-29 03:18 作者: forthy 時間: 2025-3-29 10:42 作者: HATCH 時間: 2025-3-29 12:38
Fertility Control — Update and Trends with multi-label classification is the long-tailed distribution of labels. Many studies focus on improving the overall predictions of the model and thus do not prioritise tail-end labels. Improving the tail-end label predictions in multi-label classifications of medical text enables the potential t作者: faultfinder 時間: 2025-3-29 15:38
https://doi.org/10.1007/978-4-431-55151-5 a verbalizer which constructs a mapping between label space and label word space, prompt-tuning can achieve excellent results in few-shot scenarios. However, typical prompt-tuning needs a manually designed verbalizer which requires domain expertise and human efforts. And the insufficient label spac作者: malign 時間: 2025-3-29 23:04
https://doi.org/10.1007/978-3-031-15931-2artificial intelligence; computational linguistics; computer science; computer systems; computer vision; 作者: 寄生蟲 時間: 2025-3-30 01:04
978-3-031-15930-5The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl作者: municipality 時間: 2025-3-30 07:32
Artificial Neural Networks and Machine Learning – ICANN 2022978-3-031-15931-2Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: Collision 時間: 2025-3-30 10:09
,Alleviating Overconfident Failure Predictions via?Masking Predictive Logits in?Semantic Segmentatioe an excessive overconfidence phenomenon in semantic segmentation regarding the model’s classification scores. Unlike image classification, segmentation networks yield undue-high predictive probabilities for failure predictions, which may carry severe repercussions in safety-sensitive applications. 作者: Predigest 時間: 2025-3-30 13:38 作者: 治愈 時間: 2025-3-30 18:34 作者: 潛伏期 時間: 2025-3-30 23:13
,Long-Horizon Route-Constrained Policy for?Learning Continuous Control Without Exploration,e high cost and high risk of online Reinforcement Learning. However, these solutions have struggled with the distribution shift issue with the lack of exploration of the environment. Distribution shift makes offline learning prone to making wrong decisions and leads to error accumulation in the goal作者: 不能約 時間: 2025-3-31 01:29
Model-Based Offline Adaptive Policy Optimization with Episodic Memory,, offline RL is challenging due to extrapolation errors caused by the distribution shift between offline datasets and states visited by behavior policy. Existing model-based offline RL methods set pessimistic constraints of the learned model within the support region of the offline data to avoid ext作者: 使隔離 時間: 2025-3-31 06:36
,Multi-mode Light: Learning Special Collaboration Patterns for?Traffic Signal Control,ever, existing researches generally combine a basic RL framework Ape-X DQN with the graph convolutional network (GCN), to aggregate the neighborhood information, lacking unique collaboration exploration at each intersection with shared parameters. This paper proposes a multi-mode Light model that le作者: STANT 時間: 2025-3-31 09:40 作者: Myocarditis 時間: 2025-3-31 15:23
,Reinforcement Learning for?the?Pickup and?Delivery Problem,any heuristic algorithms to solve them. However, with the continuous expansion of logistics scale, these methods generally have the problem of too long calculation time. In order to solve this problem, we propose a reinforcement learning (RL) model based on the Advantage Actor-Critic, which regards 作者: 材料等 時間: 2025-3-31 21:27 作者: 規(guī)章 時間: 2025-3-31 22:55
,Understanding Reinforcement Learning Based Localisation as?a?Probabilistic Inference Algorithm,tain a large number of labelled data, semi-supervised learning with Reinforcement Learning is considered in this paper. We extend the Reinforcement Learning approach, and propose a reward function that provides a clear interpretation and defines an objective function of the Reinforcement Learning. O作者: 亞當(dāng)心理陰影 時間: 2025-4-1 05:01
,Word-by-Word Generation of?Visual Dialog Using Reinforcement Learning, and processing visual content. While previous research focused on arranging questions to form dialog, we tackle the more challenging task of arranging questions from words, and dialog from questions. We develop our model in a simple “Guess which?” game scenario where the agent needs to predict an i作者: inconceivable 時間: 2025-4-1 06:31 作者: Anonymous 時間: 2025-4-1 10:25 作者: acetylcholine 時間: 2025-4-1 17:08 作者: Chronological 時間: 2025-4-1 20:55
,An Unsupervised Sentence Embedding Method by?Maximizing the?Mutual Information of?Augmented Text Reanner. However, unsupervised methods pale by comparison to supervised ones on many tasks. Recently, some unsupervised methods propose to learn sentence representations by maximizing the mutual information between text representations of different levels, such as global MI maximization: global and gl作者: 令人作嘔 時間: 2025-4-1 23:11
Analysis of COVID-19 5G Conspiracy Theory Tweets Using SentenceBERT Embedding,conspiracy theories in different subjects, with COVID-19 conspiracies among them. In this research, we collected a dataset of 331,448 tweets related to the COVID-19 5G conspiracy theory. We present a workflow to collect, classify, and analyze conspiracy related tweets as supporting or opposing the c
