有一个值得注意的点是，在做Umax的时候，Blake选择Partner With KOL，而在做Cal AI的时候，他们完全是Paid influencer Marketing，Blake认为，这是领域不同导致的策略不同。Cal AI所在的领域其实是Weight Loss，这是一个非常大的领域，很多头部产品立在里面，look maxing则是另外一回事情，很小的细分赛道，一个头部...
def get_M0_and_exponents(multipliers: list[tuple[int, float]]): # pylint: disable=invalid-name """ This function tries to find the best parameters for M = 2^(-n) * M0 whereby the individual n are unknown as well as the common constant M0. Only M is known. Further, M0 should be...
(60, 1)) loss = nn.MSELoss() # 0.03 直接epoch2就Nan了 0.001 有提高(bs=2) trainer = paddle.optimizer.SGD(learning_rate=0.000001, parameters=net.parameters()) num_epochs = 10 for epoch in range(num_epochs): for i,(X, y) in enumerate (data_iter()): # print(X.dtype, y.dtype...
According to social media posts by some users, Tessa sometimes gave weight-loss tips, which can be triggering to people with eating disorders. NEDA suspended the chatbot on May 30 and said in a statement that it is re...
Collins Dictionary has named “AI” as its word of the year, defining it as an “abbreviation for artificial intelligence: the modelling of human mental functions by computer programs.”《柯林斯词典》将AI选定为年度词汇。《柯林斯词典》对AI的定义是：“人工智能的缩写：通过计算机程序来模拟人类大脑功能...
ICCV 2017 Open Access Repositoryopenaccess.thecvf.com/content_iccv_2017/html/Lin_Focal_Loss_for_ICCV_2017_paper.html 1.介绍 1.1背景 Focal loss主要用于解决目标检测中难易样本不平衡问题。主流的目标检测算法分为一/二阶段。二阶段检测算法如Faster RCNN,RFCN,需要一阶段生成region proposal,速度相对较...
# init a GPT and the optimizertorch.manual_seed (1337)gpt = GPT (config)optimizer = torch.optim.AdamW (gpt.parameters (), lr=1e-3, weight_decay=1e-1)# train the GPT for some number of iterationsfor i in range (50): logits = gpt (X) loss = F.cross_entropy (logits, Y...
A 45-year-old Chaozhou man， 173cm tall and weighing 78kg， with a slightly overweight physique. As an entrepreneur， he constantly travels between Shanghai， Shenzhen， and Chongqing. Over the years， he has been troubled by hair loss. He is a humorous and playful person who dislikes being...
Training lightweight models of different networks to test for interence speed and metrics for anime upscaling with realistic degradations. See results in the corresponding results folder. LSDIR A series trained on the big LSDIR dataset. Mostly interpolated output result. Then N for no degradation,...
(this->weight_tensor_fp32, this->weight_data_fp32, this->weight_tensor_fp32->elem_num, 8, 1, weight_tensor_fp32->dims[0]);std::vector<double> cosin_save(weight_tensor_fake_quant->dims[0], -1);std::vector<float> zoom_save(weight_tensor_fake_quant->dims[0], -1);for (int ...