Speakers identifying as male, female or non-binary bring different perspectives. Gender Balance: the lack of gender equity in science is widely recognized 7. If we do not bring together scientists from all corners and cultures of the globe, we risk missing important links with our own research, failing to recognize ideas developed and insights obtained elsewhere in the world, and wasting precious time and funding on repeating work already done. In the context of major global issues that need to be addressed with urgency, this is truer now than ever before.Īll continents: representation and dissemination of and exchange between research being carried out throughout the world is of great importance. We think this must change in the interest of both science and society. Think of your own favourite conference and ask yourself what fraction of global cultures are typically represented among the keynotes, speakers and participants. Scientific culture can also be inward facing with many symposia drawing on participants repetitively from a small sector of the global community both within and beyond disciplines. However, while there are hundreds of conferences and symposia within individual research fields, the examples of those achieving this seem to be relatively few. A wide range of diverse backgrounds of participants is often seen as a key element to facilitate novel insights, promote unconventional solutions and advance science. Conferences are also important to synthesize knowledge, especially through sessions dedicated to a specific topic, to bring together different views and to explore solutions to shared problems. Nature Ecology & Evolution volume 4, pages 668–671 ( 2020) Cite this articleĬonferences are, next to publications in scientific journals, the most commonly used format to present and disseminate recent advances in scientific research. Memory format tag specification.A meeting framework for inclusive and sustainable science Struct dnnl::threadpool_interop::threadpool_ifaceĮnum dnnl::memory::format_tag ¶ Overview ¶ Struct dnnl::resampling_forward::primitive_descĮnum dnnl::graph::logical_tensor::data_typeĮnum dnnl::graph::logical_tensor::layout_typeĮnum dnnl::graph::logical_tensor::property_type Struct dnnl::resampling_backward::primitive_desc Struct dnnl::vanilla_rnn_forward::primitive_desc Struct dnnl::vanilla_rnn_backward::primitive_desc Struct dnnl::lstm_forward::primitive_desc Struct dnnl::lstm_backward::primitive_desc Struct dnnl::lbr_gru_forward::primitive_desc Struct dnnl::lbr_gru_backward::primitive_desc Struct dnnl::lbr_augru_forward::primitive_desc Struct dnnl::lbr_augru_backward::primitive_desc Struct dnnl::gru_backward::primitive_desc Struct dnnl::augru_forward::primitive_desc Struct dnnl::augru_backward::primitive_desc Struct dnnl::inner_product_forward::primitive_desc Struct dnnl::inner_product_backward_weights::primitive_desc Struct dnnl::inner_product_backward_weights Struct dnnl::inner_product_backward_data::primitive_desc Struct dnnl::layer_normalization_forward::primitive_desc Struct dnnl::layer_normalization_backward::primitive_desc Struct dnnl::layer_normalization_backward Struct dnnl::batch_normalization_forward::primitive_desc Struct dnnl::batch_normalization_backward::primitive_desc Struct dnnl::batch_normalization_backward Struct dnnl::lrn_backward::primitive_desc Struct dnnl::prelu_forward::primitive_desc Struct dnnl::prelu_backward::primitive_desc Struct dnnl::pooling_forward::primitive_desc Struct dnnl::pooling_backward::primitive_desc Struct dnnl::softmax_forward::primitive_desc Struct dnnl::softmax_backward::primitive_desc Struct dnnl::eltwise_forward::primitive_desc Struct dnnl::eltwise_backward::primitive_desc Struct dnnl::shuffle_forward::primitive_desc Struct dnnl::shuffle_backward::primitive_desc Struct dnnl::deconvolution_forward::primitive_desc Struct dnnl::deconvolution_backward_weights::primitive_desc Struct dnnl::deconvolution_backward_weights Struct dnnl::deconvolution_backward_data::primitive_desc Struct dnnl::convolution_forward::primitive_desc Struct dnnl::convolution_backward_weights::primitive_desc Struct dnnl::convolution_backward_weights Struct dnnl::convolution_backward_data::primitive_desc Using oneDNN with Threadpool-Based Threading Transitioning from Intel MKL-DNN to oneDNN Primitive Attributes: floating-point math mode
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