Initialization
Neural networks and the case for Mostly Right.
How you begin can determine how well you end up.
In machine learning, initialization is like setting the starting point for a journey. Weights are usually initialized from a random distribution, while biases are usually initialized to zeros or small random values. These initial conditions determine:
How quickly you end up at the right destination (convergence)
How easily you get stuck in the wrong places (local minima)
How erratic or divergent your journey is (stability)
How much redundancy (symmetry) you’ll experience
How likely you’ll explore and find the best path in the parameter space
The right initial conditions can significantly accelerate training and improve model performance. The wrong initial conditions can cause numerical instability, lead to suboptimal solutions, and result in failed training attempts (and wasted compute).1
When I started dating last year, a well-intentioned friend recommended Marry Him: The Case for Settling for Mr. Good Enough by Lori Gottlieb. When I got the book, I promptly put it to use as laptop stand on my desk. This friend is now engaged to a loving, thoughtful partner. I am still assembling my own furniture and getting dangerously good at opening my own jars.2
On occasion, I think about Lori’s case for “Good Enough”.
I know little about building neural networks. I know even less about what it takes to sustain a decades-long partnership.
Still, these are the things I believe to be true:
There is no calculus to derive “Good Enough”.
There is only what we are willing to accept, trade off, and work with.
Someone is special because we choose to believe they are special.3
We make them special. That’s it. That’s the secret.
I’m back in Austin this week, where my IB analyst class met for a reunion last June. When it came up that I was single and dating, one of them—a hard-drinking, hard-partying portfolio manager whose career is ascendant and envious—pulled me aside and looked me square in the eyes.
“Love is the hardest market you’ll ever have to operate in,” he said. “There are no good men or bad men. There are just men. If you want a family, you have to do your diligence then decide. If you do this right, you only have to do this once.”
I remember studying him soberly, wondering who kidnapped the 23-year-old kid in freshly tailored Brooks Brothers who crushed Natty Lights on weekends and replaced him with this lucid, clear-eyed stranger who spoke with the quiet confidence of someone who’d done the work.
“So he better be Mostly Right.”
“What if I don’t find that?”
“Well then you’re stuck with us!”
And suddenly we were twenty-three again, goofing off in the bullpen, sneaking into the MD’s corner office to watch the sunrise over the East River from the 54th floor, and playing pranks that would get us in (only mildly questionable) trouble with the 2nd-year Analysts and Associates around the office. And I thought: Good Enough.
All brilliance credited to ChatGBT. More on the importance of effective initialization.
I’m just a step stool away from declaring complete domestic independence. 🙂
Shout-out to KV for reacquainting me with the magic of Kung Fu Panda.