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Sampled Softmax

sampled softmax原论文:On Using Very Large Target Vocabulary for Neural Machine Translation 以及tensorflow关于 (关于其它的解决方法,作者也有提,感兴趣的可以看原文,本篇博客只关注Sampled Softmax)2.

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Zipkin客户端链路追踪源码解析

= null) { Boolean sampled = traceIdContext.sampled(); if (sampled == null) sampled = sampler.isSampled (traceIdContext.traceId()); return toSpan(TraceContext.newBuilder() .sampled(sampled) .debug(traceIdContext.debug HexCodec.toLowerHex(currentSpan.spanId()); MDC.put(spanId, spanId); MDC.put(LEGACY_SPAN_ID_NAME, spanId); String sampled = String.valueOf(currentSpan.sampled()); MDC.put(spanExportable, sampled); MDC.put(LEGACY_EXPORTABLE_NAME , sampled); log(Starting scope for span: {}, currentSpan); if (currentSpan.parentId() !

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    分布式链路追踪系统原来是这么一回事

    带内数据被称为 b3-propagation,包含 TraceId,SpanId,ParentSpanId,Sampled四个字段,每个server在生成span之后会得到TraceId,SpanId, Zipkin的采样字段Sampled有四种状态 DeferDenyAcceptDebug,采样的一个重要前提是下游要尊重上游的采样决定,不能随意更改sampled字段。 Defer代表该span的采样状态还未决定,下游收到该状态时则可以对sampled字段重新赋值。 Deny代表该span不上报。 Accept代表span需要上报。 Root_span的sampled字段由系统的采样率来决定。如采样率为50%,则一半的带内数据中sampled字段为accept,其他为deny。 并记录kind为CLIENT,name,timestamp,localendpoint(server-2)信息,并将traceid,id,parentid,sampled信息传递给server-3。

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    torch.nn.init

    The resulting tensor will have values sampled from ? where?​ The resulting tensor will have values sampled from ? where?​ The resulting tensor will have values sampled from ? where?​ The resulting tensor will have values sampled from ? where?​

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    遇到曝光偏差怎么办?用对比学习!

    论文中使用的对比loss类似Sampled Softmax,先看看Sampled Softmax: L表示负采样的个数,pn(y|x)是预先定义好的负采样分布,减去logpn(y|x)是为了让该loss 当候选集巨大时,sampled softmax效果要优于NCE和negative sampling。 实验论文中的实验持续了至少4个月,离线评估纠偏时,比较了sampled softmax和CLRec,在不同loss下我们可以看到CLRec显著提高了多样性(提升了1倍),并且从曝光分布我们可以看到sampled

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    istio-3:istio-1.4.2-demo链路体验之jaeger

    70581e6826c6b85d142280f15bd5e0eb, X-B3-Spanid:d1c1b20bbbfc2c68, X-B3-Parentspanid: 142280f15bd5e0eb,X-B3-Sampled 0,n Host:httpbin:8000, n User-Agent: Python-urllib3.6, n X-B3-Parentspanid:d5ed195b2fb6fb97, n X-B3-Sampled 70581e6826c6b85d142280f15bd5e0eb, X-B3-Spanid:d1c1b20bbbfc2c68, X-B3-Parentspanid:142280f15bd5e0eb, X-B3-Sampled 8a211f31f620a1bff0eff6b65f626b90, X-B3-Spanid:d242b2985286bb23, X-B3-Parentspanid:f0eff6b65f626b90, X-B3-Sampled 0,n Host:httpbin:8000, n User-Agent: Python-urllib3.6, n X-B3-Parentspanid:eb582674eccb9c14, n X-B3-Sampled

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    点云蒙特卡罗卷积网络Monte Carlo Convolution

    标题:Monte Carlo Convolution for Learning on Non-Uniformly Sampled Point Clouds作者:Hermosilla, P. and Ritschel We propose an efficient and effective method to learn convolutions for non-uniformly sampled point clouds the robustness of our method with respect to sampling variations, even when training with uniformly sampled

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    torch09:variational_autoencoder(VAE)--MNIST和自己数据集

    data_loader), reconst_loss.item(), kl_div.item())) # 模型测试部分 # 测试阶段不需要计算梯度,注意 with torch.no_grad(): # Save the sampled z_dim).to(device) out = model.decode(z).view(-1, 1, 28, 28) save_image(out, os.path.join(sample_dir, sampled data_loader), reconst_loss.item(), kl_div.item())) # 模型测试部分 # 测试阶段不需要计算梯度,注意 with torch.no_grad(): # Save the sampled z_dim).to(device) out = model.decode(z).view(-1, 1, 28, 28) save_image(out, os.path.join(sample_dir, sampled

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    Object.assign 和 Object Spread 之争, 用谁?

    map(name)); }). run({ async: true });在这种情况下,两者是相似的:Object spread x 3,170,111 opssec +-1.50% (90 runs sampled )Object.assign() x 3,290,165 opssec +-1.86% (88 runs sampled)Fastest is Object.assign()但是,一旦向 Object.assign function() { Object.assign({}, obj, { baz: 3 }); })这是输出:Object spread x 3,065,831 opssec +-2.12% (85 runs sampled )Object.assign() x 2,461,926 opssec +-1.52% (88 runs sampled)Fastest is Object spreadESLint 配置默认情况下,ESLint

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    tensorflow 0.10 word2vec 源码解析

    .} # We sum out true and sampled losses. return _sum_rows(sampled_losses) #在word2vec中对此函数的返回调用了reduce_mean NOTE: pylint cannot tell that sampled_values is a sequence # pylint: disable=unpacking-non-sequence sampled sampled_expected_count = sampled_values # pylint: enable=unpacking-non-sequence # labels_flat is a tensor # sampled sampled_b if remove_accidental_hits: acc_hits = candidate_sampling_ops.compute_accidental_hits( labels, sampled validate_indices=False) if subtract_log_q: # Subtract log of Q(l), prior probability that l appears in sampled

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    Jaeger的客户端采样配置(Java版)

    jaeger-service-provider和服务调用方jaeger-service-consumer,都做成docker镜像,用docker-compose启动,网络架构如下图:请确保项目的日志模板中已添加了traceId、spanId、sampled full.sh脚本,编译构建部署 浏览器访问http:localhost:18080hello,产生一些web请求,多访问几次 看jaeger-service-consumer容器的日志,如下图,红框中的sampled Controller,如下图红框所示:向jaeger-service-consumer的hello接口发送完一百次请求后,可以从docker容器日志中检查采样情况,这里使用grep和wc命令的组合来统计日志中出现sampled =true和sampled=false的行数,完整的命令如下:docker logs jaeger-service-consumer| grep sampled=true|wc -l100个请求,采样率百分之十 ,我这里设置为10秒:用jmeter持续发送10秒的请求,从jmeter的汇总报告中可见一共发了70个请求:用命令docker logs jaeger-service-consumer| grep ‘sampled

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    SCUT-HEAD-Dataset-Release

    PartA includes 2000 images sampled from monitor videos of classrooms in an university with 67321 heads PartAPartA includes 2000 images sampled from monitor videos of classrooms in an university with 67321

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    R2D2算法介绍

    LSTM from replayed experience:• Using a zero start state to initialize the network at the beginning of sampled because of the highly correlated nature of states in a trajectory when compared to training on randomly sampled issues, we propose and evaluate two strategies for training a recurrent neural network from randomly sampled For that, we will compare the Q values produced by the network on sampled replay sequences when unrolled

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    基于多样性的轨迹和目标选择与事后经验回放(cs.LG)

    In HER, both trajectories and transitions are sampled uniformly for training. Firstly, trajectories are sampled according to the diversity of the goal states as modelled by determinantal

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    神经序贯项目推荐模型抽样策略评价的实例研究

    The target set contains the relevant item and a set of negative items that are sampled from the full Then we evaluate all models on a target set sampled by the two different sampling strategies, uniform Additionally, we vary the size of the sampled target set.

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    11g升级性能问题之一 重建user_synonyms (笔记27天)

    49.58PRDOPRC sqlplus@host1(TNS V1-V3) 119120 0^LTop SQL with Top Events DBInst: (Aug 23 12:30 to 12:32) Sampled ---------------------------------------Top SQL with Top Row Sources DBInst: (Aug 23 12:30 to 12:32) Sampled

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    理解深层神经网络中的迁移学习及TensorFlow实现

    validation_bottlenecks, ground_truth_input: validation_ground_truth}) print(Step %d: Validation accuracy on random sampled Step 1000: Validation accuracy on random sampled 100 examples = 92.0% . . Step 2700: Validation accuracy on random sampled 100 examples = 94.0% . . Step 3999: Validation accuracy on random sampled 100 examples = 94.0% Final test accuracy = 92.7%从结果可以看到

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    CaSPR: 学习规范的时空点云表示(CS)

    representations that support spacetime continuity, are robust to variable and irregularly spacetime-sampled spatiotemporal sequence reconstruction, and correspondence estimation from irregularly or intermittently sampled

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    2020顶级会议回顾 | 从这些最佳论文中总结研究趋势(文中附论文下载链接)

    KDD 2020最佳论文:On Sampled Metrics for Item Recommendation论文链接:http:walid.krichene.netpapersKDD-sampled-metrics.pdf

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    聊聊skywalking的SamplingService

    . * If many distributed traces require sampled, * the trace beginning at local, has less chance to be sampled. * public void forceSampled() { if (on) { samplingFactorHolder.incrementAndGet(); } } private

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