Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2022; 31st International C Elias Pimenidis,Plamen Angelov,Mehmet Aydin Conference p

osculate

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Common-Migraine

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

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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

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,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

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,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

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expansive

,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.

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Condyle

,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