使用的第三个数据集是Blue Hexagon Open Dataset for Malware Analysis (BODMAS),其中包括EMBER、SOREL-20M、和一些更新更好的标记数据。 本报告分析了在这些基准数据集上训练机器学习模型时产生的分布偏移和缓解措施。使用LightGBM作为人工智能方法,使用KS...
使用的第三个数据集是Blue Hexagon Open Dataset for Malware Analysis (BODMAS),其中包括EMBER、SOREL-20M、和一些更新更好的标记数据。 本报告分析了在这些基准数据集上训练机器学习模型时产生的分布偏移和缓解措施。使用LightGBM作为人工智能方法,使用KS...
Randulate is an npm package that simplifies the process of generating fake data for development, testing, and various other use cases. It allows you to create randomized data based on provided specifications and data types. Please note that this package is a work in progress, and I'm actively...
Randulate is an npm package that simplifies the process of generating fake data for development, testing, and various other use cases. It allows you to create randomized data based on provided specifications and data types. Please note that this package is a work in progress, and I'm actively...
In the random exposure stage, each recommended video in the dataset has an equal probability of being replaced by a random video sampled from an item pool. About $0.37\%$ Interactions are replaced in the final results. Advantages: Compared with other datasets with random exposure, KuaiRand has...
In the random exposure stage, each recommended video in the dataset has an equal probability of being replaced by a random video sampled from an item pool. About $0.37\%$ Interactions are replaced in the final results. Advantages: Compared with other datasets with random exposure, KuaiRand has...
Returns an array of strings, where each string represents the name of a country available in the dataset. Example: const Randulate = require('randulate'); const allCountries = listAllCountries(); getTotalPopulation() Calculates and returns the total approximate population of all countries. Return...
12142 【PyTorch】torch.utils.data.DataLoader 2019-12-09 16:09 −torch.utils.data.DataLoader 简介 DataLoader是PyTorch中的一种数据类型。对数据进行按批读取。 使用Pytorch自定义读取数据时步骤如下:1)创建Dataset对象2)将Dataset对象作为参数传递到Dataloader中 ... ...
RandHistogramShiftD(KEYS, prob=1, num_control_points=30, allow_missing_keys=True),#ToTensorD(KEYS),])defforward(self,x): x=self.random_rotated(x)returnx#start a datasetdefsave(before_x, after_x, new_path,new_name=""): after_x=after_x[0, 0,...]ifnew_name=="image": ...
def get_input(sample): template = dataset2instruction[sample["subtask_type"]] # print(template) # print(sample) sample["input"] = template["prompt"].format(*[ sample[k] for k in template["keys_order"] ]) print(sample["input"]) return sample["input"] 收集足够多的有监督数据集是提升...