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It turns out that you can drop a lot of weights from your neural network without seeing a drastic effect on accuracy. In this video ...

In the previous video, we've started measuring bias in word embeddings. In this video, we will attempt to remove some of this bias ...

In this video, we'll highlight a qualitative argument of why you may not need to worry about pre-trained embeddings too much.

Sometimes you can change the intent labels to get a more accurate model. In this video, we will explore how to do this with a ...

What if we design a policy mechanism that doesn't predict the next action to take, but instead predicts when something ...

We're starting a new playlist! Introducing the

This is the first video on attention mechanisms. We'll start with self attention and end with transformers. We're going at it step by ...

In the age of deep learning and transformers, rule-based systems can still be a great idea. In this video, we hope to demonstrate ...

It's easy to get distracted when you go down the rabbit hole of performance statistics. But! Not every impressive benchmark is ...

This is the second video on attention mechanisms. In the previous video we introduced self attention and in this video we're going ...

In the previous video on bias, we showed that you should be careful with debiasing techniques. In this video, we zoom in on why ...

In the previous videos, we've started measuring bias in word embedding and tried to counteract the bias with a linear algebra ...

In this video, we will discuss some technical details that are in the UnexpecTEDIntentPolicy. The UnexpecTEDIntentPolicy really is ...