vkalavri@bu.edu CS 591 K1: Data Stream Processing and Analytics Spring 2020 2/06: Notions of time and progress Vasiliki Kalavri | Boston University 2020 Mobile game application • input stream: Vasiliki Kalavri | Boston University 2020 • Processing time • the time of the local clock where an event is being processed • a processing-time window wouldn’t account for game activity while the train Event time • the time when an event actually happened • an event-time window would give you the extra life • results are deterministic and independent of the processing speed Notions of time 5 Vasiliki

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similar to the baseline, but does so in fewer epochs. We could ideally save an epoch’s worth of training time by terminating the training early, if we adopt this hypothetical sample efficient model training. effective utilization of the training data. Labeling data is often an expensive process both in terms of time consumption and fiscal expenditure because it involves human labelers looking at each example and the four classes, three of which are the keywords that the device will accept: hello, weather and time. The fourth class (none) indicates the absence of an acceptable keyword in the input signal. Figure

toronto.edu/~fritz/absps/cvq.pdf probability distributions Meanwhile: Speech Sequence ▪ No Memory ▪ Time delay NN http://www.cs.toronto.edu/~fritz/absps/waibelTDNN.pdf Moving window ▪ Inspired LeCun Vector Machine: 1992 http://www.iro.umontreal.ca/~vincentp/ift3395/lectures/backprop_old.pdf Dark time ▪ Paper got rejected ▪ Hinton moved to CIFAR seeking for funding ▪ Conspiracy: rebrand“neural

is, it relies on the momentum of the weights which is an exponentially smoothed estimate of over time. For instance, the momentum of weight at training step is given by: 2 Dettmers, Tim, and Luke Zettlemoyer scores, but they will all try to approximate the importance of a given weight at a certain point of time in the training process to minimize the loss function. The better we can estimate this importance granularities visually. Figure 5-4: An example of sparsified weight matrices (zero-d weights are dark) each with 33% sparsity at various granularity levels. It shows the parameter layout for a convolutional

iptables- min-sync-period array 刷新 iptables 规则前的最短持续时间。此字段确保刷 新的频率不会过于频繁。有效的后缀包括 s、m 和 h，具体参见 Go time 软件包。默认值为： 5.5.2. Cluster Network Operator 配置示例 以下示例中指定了完整的 CNO 配置： Cluster Network Operator 对象示例 以可读格式提供状态的原因。这个值是 TCPConnect、TCPConnectError、DNSResol ve、DNSError 之一。 success 布 布尔值 尔值 指明日志条目是否成功或失败。 time 字符串 字符串 连接检查的开始时间。 字段 字段 类 类型 型 描述 描述 12.4. 验证端点的网络连接 作为集群管理员，您可以验证端点的连接性，如 API 服务器、负载均衡器、服务或 Pod。 refused' reason: TCPConnectError success: false 第 第 12 章 章 验证 验证到端点的 到端点的连 连接 接 101 time: "2021-01-13T20:10:34Z" - latency: 2.582129ms message: 'kubernetes-apiserver-endpoint-ci-ln

算在内 cpu_time = timeit.timeit(cpu_run, number=3) gpu_time = timeit.timeit(gpu_run, number=3) print('warmup:', cpu_time, gpu_time) # 正式计算 10 次，取平均时间 cpu_time = timeit.timeit(cpu_run timeit(cpu_run, number=10) 预览版202112 第 1 章 人工智能绪论 16 gpu_time = timeit.timeit(gpu_run, number=10) print('run time:', cpu_time, gpu_time) 将不同大小?下的 CPU 和 GPU 环境的运算时间绘制为曲线，如图 1.21 所示。可以看 到，在矩阵?和矩阵 Fort Lauderdale, FL, USA, 2011. [3] J. Mizera-Pietraszko 和 P. Pichappan, Lecture Notes in Real-Time Intelligent Systems, Springer International Publishing, 2017.

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164 2.1.9 Time series . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167 Exponentially Weighted window . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 832 2.20 Time series / date functionality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 836 2.20.2 Timestamps vs. time spans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 837 2.20.3 Converting

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160 2.1.9 Time series . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162 Exponentially Weighted window . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 798 2.20 Time series / date functionality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 802 2.20.2 Timestamps vs. time spans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 803 2.20.3 Converting

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164 2.1.9 Time series . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167 Exponentially Weighted window . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 831 2.20 Time series / date functionality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 835 2.20.2 Timestamps vs. time spans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 836 2.20.3 Converting