diff --git a/_pages/bingham-rotation-learning.md b/_pages/bingham-rotation-learning.md
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[ View it on Github](https://github.com/utiasSTARS/bingham-rotation-learning){: .btn .btn-green }
-There are many ways to represent rotations: Euler angles, rotation matrices, axis-angle vectors, or unit quaternions, for example. In deep learning, it is common to use unit quaternions for their simple geometric and alebraic structure. However, unit quaternions lack an important smoothness property that makes learning 'large' rotations difficult, and other representations are not easily amenable to learning uncertainty. In this work, we address this gap and present a smooth representation that defines a belief (or distribution) over rotations.
+There are many ways to represent rotations: Euler angles, rotation matrices, axis-angle vectors, or unit quaternions, for example. In deep learning, it is common to use unit quaternions for their simple geometric and algebraic structure. However, unit quaternions lack an important smoothness property that makes learning 'large' rotations difficult, and other representations are not easily amenable to learning uncertainty. In this work, we address this gap and present a smooth representation that defines a belief (or distribution) over rotations.
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