退火式多选择学习：通过退火克服赢家通吃的局限性
Annealed Multiple Choice Learning: Overcoming limitations of Winner-takes-all with annealing
AI总结：
针对多选择学习中赢家通吃机制易收敛到次优局部最小值的问题，提出结合模拟退火的aMCL方法以增强假设空间探索，并通过多组实验验证了算法有效性。
摘要：
我们提出了退火式多选择学习（Annealed Multiple Choice Learning，aMCL），该方法将模拟退火与MCL（多选择学习）相结合。MCL是一种通过预测一小套合理假设来处理模糊任务的学习框架。这些假设采用赢家通吃（Winner-takes-all，WTA）机制训练，该机制可提升预测的多样性。但由于WTA的贪婪特性，该机制可能会收敛到任意次优的局部最小值。我们利用退火技术克服了这一局限，退火可增强训练过程中对假设空间的探索。我们借助统计物理学和信息论的见解，对模型训练轨迹进行了详细描述。此外，我们通过在合成数据集、标准UCI基准以及语音分离任务上开展大量实验，验证了该算法的有效性。
英文摘要：
We introduce Annealed Multiple Choice Learning (aMCL) which combines simulated annealing with MCL. MCL is a learning framework handling ambiguous tasks by predicting a small set of plausible hypotheses. These hypotheses are trained using the Winner-takes-all (WTA) scheme, which promotes the diversity of the predictions. However, this scheme may converge toward an arbitrarily suboptimal local minimum, due to the greedy nature of WTA. We overcome this limitation using annealing, which enhances the exploration of the hypothesis space during training. We leverage insights from statistical physics and information theory to provide a detailed description of the model training trajectory. Additionally, we validate our algorithm by extensive experiments on synthetic datasets, on the standard UCI benchmark, and on speech separation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
