Neural networks trained to classify images have a remarkable — and surprising! — capacity to generate images. Techniques such as DeepDream , style transfer,…
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Feature-wise transformations
Many real-world problems require integrating multiple sources of information. Sometimes these problems involve multiple, distinct modalities of information — vision, language, audio,…
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The Building Blocks of Interpretability
With the growing success of neural networks, there is a corresponding need to be able to explain their decisions — including building…
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Using Artificial Intelligence to Augment Human Intelligence
What are computers for? Historically, different answers to this question – that is, different visions of computing – have helped…
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Sequence Modeling with CTC
Introduction Consider speech recognition. We have a dataset of audio clips and corresponding transcripts. Unfortunately, we don’t know how the…
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Feature Visualization
There is a growing sense that neural networks need to be interpretable to humans. The field of neural network interpretability…
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Why Momentum Really Works
Step-size α = 0.02 Momentum β = 0.99 We often think of Momentum as a means of dampening oscillations and…
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Research Debt
Achieving a research-level understanding of most topics is like climbing a mountain. Aspiring researchers must struggle to understand vast bodies…
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একটি নিউরাল নেটওয়ার্কের সাথে হাতে লেখা পরীক্ষা
আপনার হাতের লেখা ইনপুটের উপর ভিত্তি করে নতুন স্ট্রোক তৈরি করে শুরু করা যাক নিউরাল নেটওয়ার্কগুলি মেশিন লার্নিংয়ের একটি অত্যন্ত…
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Deconvolution and Checkerboard Artifacts
When we look very closely at images generated by neural networks, we often see a strange checkerboard pattern of artifacts.…
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