Samuel Mueller
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04eb228
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1ca0822
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app.py
CHANGED
@@ -100,6 +100,7 @@ iface = gr.Interface(fn=infer,
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Lines represent means and shaded areas are the confidence interval (68.2% quantile). In green, we have the ground truth GP posterior and in blue we have our approximation.
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We provide three models that are architecturally the same, but with different training budgets.
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''',
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inputs=[
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gr.inputs.Dataframe(headers=["x", "y"], datatype=["number", "number"], row_count=2, type='numpy', default=[['.25','.1'],['.75','.4']], label='The data: you can change this and increase the number of data points using the `enter` key.'),
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gr.inputs.Radio(['160K','800K','4M'], type="value", default='160K', label='Number of Sampled Datasets in Training (Training Costs)')
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Lines represent means and shaded areas are the confidence interval (68.2% quantile). In green, we have the ground truth GP posterior and in blue we have our approximation.
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We provide three models that are architecturally the same, but with different training budgets.
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''',
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article="<p style='text-align: center'><a href='https://openreview.net/forum?id=KSugKcbNf9'>Paper: Transformers Can Do Bayesian Inference</a></p>",
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inputs=[
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gr.inputs.Dataframe(headers=["x", "y"], datatype=["number", "number"], row_count=2, type='numpy', default=[['.25','.1'],['.75','.4']], label='The data: you can change this and increase the number of data points using the `enter` key.'),
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gr.inputs.Radio(['160K','800K','4M'], type="value", default='160K', label='Number of Sampled Datasets in Training (Training Costs)')
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