关于temperature parameter的解释可以看这里面的回答,本文只着重于对比学习里面infoNCE loss中temperature参数的理解。 What is the temperature parameter in deep learning?www.quora.com/What-is-the-temperature-parameter-in-deep-learning SimCLR论文中指出: an appropriate temperature can help the model learn fr...
在计算机视觉任务中,Full fine-tuning 通常直接去fine tune整个预训练模型,这会带来较大的计算资源的需求。 受NLP中相关工作的启发(如Parameter-Efficient Transfer Learning for NLP),作者探索了在CV任务中使用Adapter 进行 parameter-efficient transfer learning的可能性。 不同于 full fine tune直接更新整个模型,Adapt...
第一周:深度学习的实践层面 (Practical aspects of Deep Learning) 1.1 训练,验证,测试集(Train / Dev / Test sets) 创建新应用的过程中,不可能从一开始就准确预测出一些信息和其他超级参数,例如:神经网络分多少层;每层含有多少个隐藏单元;学习速率是多少;各层采用哪些激活函数。应用型机器学习是一个高度迭代的...
batch size must be examined in conjunction with the execution time of the training. The batch size is limited by your hardware’s memory, while the learning rate is not. Leslie recommends using a batch size that fits in your hardware’s memory and enable using larger learning rates. ...
Unsupervised deep learning is one of the most powerful representation learning techniques. Restricted Boltzman machine, sparse coding, regularized auto-encoders, and convolutional neural networks are pioneering building blocks of deep learning. In this paper, we propose a new building block -- distribute...
Week 1 Quiz Practical aspects of deep learning(第一周测验 深度学习的实践) \1. If you have 10,000,000 examples, how would you split the train/dev/test set? (如果你有 1
We thus propose a deep reinforcement learning and parameter transfer based approach (RLPT) to tackle the MO-AEOSSP in a non-iterative manner. RLPT first decomposes the MO-AEOSSP into a number of scalarized sub-problems by a weight sum approach where each sub-problem can be formulated as ...
\9. Suppose batch gradient descent in a deep network is taking excessively long to find a value of the parameters that achieves a small value for the cost function$ 𝑱(𝑾^{[𝟏]} ,𝒃 ^{[𝟏]} ,… ,𝑾^{[𝑳]} , 𝒃^{[𝑳]} ).Whichofthefollowingtechniquescouldhelpfindpar...
Competing Metrics in Deep Learning: Accuracy vs. Inference Time In deep learning, a common tradeoff is between model accuracy and speed of making a prediction. A more complex network may be able to achieve higher accuracy, but at the cost of slower inference time due to the greater computation...
PriorBand: Practical Hyperparameter Optimization in the Age of Deep Learning Hyperparameters of Deep Learning (DL) pipelines are crucial for their downstream performance. While a large number of methods for Hyperparameter Optimizati... N Mallik,E Bergman,C Hvarfner,... 被引量: 0发表: 2023年 ...