diff --git a/Chapters/16-Energy-Based-Models/INDEX.md b/Chapters/16-Energy-Based-Models/INDEX.md new file mode 100644 index 0000000..93e7de1 --- /dev/null +++ b/Chapters/16-Energy-Based-Models/INDEX.md @@ -0,0 +1,249 @@ +# Energy Based Models + +These models takes 2 inputs, one of which is the . Then they are +passed through another function that output their compatibility, or +***"goodness"***[^stanford-lecun]. The lower the value, the better the +compatibility. + +The objective of this model is finding the most compatible value for the +energy function across all possible values: + +$$ +\hat{y} = \argmin_{y \in \mathcal{Y}}(E(x, \mathcal{Y})) \\ +x \coloneqq \text{ network output | } \mathcal{Y} \coloneqq +\text{ set of possible labels} +$$ + +The difficult part here is choosing said $\hat{y}$ as the space $\mathcal{Y}$ may +be too large, or even infinite, and there could be multiple $y$ that may have the +same energy, so multiple solutions. + +Since we want to model our function $E(x, y)$, it needs to change according to +some weights $W$. Since each time we change $W$ we are changing also the output +of $E(x, y)$, we are technically defining a family of energy functions. + +However, not all of them give us what we need, thus, by tuning $W$, we explore +this space to find a $E_W(x, y)$ that gives acceptable results. + +During inference, since we will have built a function with a structure +specifically crafted to be like that, we will generate a $y^*_0$ randomically +and then we will update it through the gradient descent + +> [!TIP] +> If this thing is unclear, think that you have fixed your function $E_{W}$ and +> notice that $x$ is unchangeable too. During gradient descent, you can update +> the value of $y^*$ by the same algorithm used for $W$. +> +> Now, you are trying to find a minimum for $E_W$, meaning that $y^*$ will be +> the optimal solution when the energy becomes 0 or around it. + +## Designing a good Loss[^stanford-lecun] + +> [!NOTE] +> +> - $y_i$: correct label for $x_i$ +> - $y^*_i$: lowest energy label +> - $\bar{y}_i$: lowest energy incorrect label + +### Using the energy function + +Since we want the energy for $y_i$ to be 0 for $x_i$, everything else is a loss for +us: + +$$ +L_i(y_i, E_{W}(x_i, y_i)) = E_{W}(x_i, y_i) +$$ + +However, this function does not increase energy for other example, possibly +resulting in the constant function: + +$$ +E_W(x_i, y_j) = 0 \,\, \forall i \in {X}, \forall j \in {Y} +$$ + +In other words, it gives 0 for all possible labels, resulting in a collapsed plane +where all energies are equal. + +### Generalized Perceptron Loss + +Another way is to give a lower bound for point $y_i$ and pushing away everything +else: + +$$ +L_i(y_i, E_{W}(x_i, y_i)) = E_{W}(x_i, y_i) - \min_{y \in \mathcal{Y}}E_{W}(x_i, y) +$$ + +When the loss becomes 0, it means that both terms are equal, meaning that +$E(x_i, y_i)$ has the lowest value. However this doesn't imply that there's not +anothe $y_j$ so that $E(x_i, y_i) = E(x_i, y_j)$, implying that this method is +still susceptible to flat planes. + +### Good losses + +To avoid the problem of a collapsed plane, we can use several losses: + +- [**Hinge Loss**](../4-Loss-Functions/INDEX.md#hingeembeddingloss) +- **Log Loss** +- **MCE Loss** +- **Square-Square Loss** +- **Square-Exponential Loss** +- [**Negative Log-Likelihood Loss**](../4-Loss-Functions/INDEX.md#nllloss) +- **Minimum Empirical Error Loss** + +All of these operates to increase the distance between $y_i$ and $\bar{y}_i$ so +that the *"best incorrect answer"* is at least $margin$ away from the correct one(s). + +> [!WARNING] +> Negative Log Likelihood makes the plane way too harsh, making it like a ravine +> where good values are in the ravine and bad ones are on top[^anelli-15] + +## Energy Based Model Architectures + +> [!CAUTION] +> Here $x$, $y$ may be scalars or vectors + +![energy based model architectures](./pngs/emb-architectures.png) + +### Regression + +The energy function for a regression is simply: + +$$ +E_W(x, y) = \frac{1}{2}|| G_W(x) - y ||_2^2 +$$ + +This architecture, during training will modify $W$ so that $G_W(x_i) \sim y_i$ and +way different from all the other $y_j$. +During inference, $y^*_i$ will be the one that is the most similar to $G_W(x_i)$ + +### Implicit Regression + +We usually use this architecture when we want more than one possible answer. +The trick is to map each admissible $y$ to the same trasnformation of $x_i$: + +$$ +E_W(x, y) = \frac{1}{2}|| G_{W_X}(x) - G_{W_Y}(y) ||_2^2 +$$ + +During training both $G_{W_X}(x_i) \sim G_{W_Y}(\mathcal{Y}_i)$, while during +inference we will choose a value from $\mathcal{Y}^*_i$ +that will have the least energy + +### Binary Classification + +For binary classification problems, we have: + +$$ +E_W(x, y) = - yG_W(x) +$$ + +During training it will make $G_W(x_i) \gt 0$ for $y_i = 1$ and viceversa. During +inference, $y_i^*$ will have the same sign of $G_W(x_i)$. + +### Multi-Class Classification + +For multiclass classification we will have: + +$$ +E_W = \sum_{k = 0}^{C} \delta(y - k) \cdot G_W(x_i)_{[k]} \\ +\delta(u) \coloneqq \text{ Kronecker impulse - } \begin{cases} + 1 \rightarrow u = 0 \\ + 0 \rightarrow u \neq 0 +\end{cases} +$$ + +This is just a way to say that $G_W(x_i)$ will produce several scores, an +array of values, and the energy will be equal to the one for the class $y_i$. + +During training $G_W(x_i)_{[k]} \sim 0$ for $y_i = k$ and all the other will +become high. During inference $y^*_i = k$ for the lowest $k^{th}$ value of +$G_W(x_i)$ + +## Latent-Variable Architectures + +We introduce in the system a *"latent"* variable $z$ that will help our model +to get more details. We never receive this value, nor it is generated based on +inputs[^yt-week-7]: + +$$ +\hat{y}, \hat{z} = \argmin_{y,z} E(x, y, z) +$$ + +However, if you like to find it only by looking at $y$ and using a probabilistic +approach, we get: + +$$ +\hat{y} = \argmin_y \lim_{\beta \rightarrow \infin} - \frac{1}{\beta} + \log \int_{z} e^{-\beta E(x, y, z)} +$$ + +An advantage is that if we operate over a set $\mathcal{Z}$, we get a set +$\hat{\mathcal{Y}}$ of predictions. However, we need to limit the informativeness +of our variable $z$, otherwise it is possible to perfectly predict $y$, + +## Relation between probabilities and Energy + +We can think of energy and probablity of being the same thing, but over +2 different points of view: + +$$ +P(y^* | x) = \frac{ + \underbrace{e^{-\beta E(x, y^*)}}_{\text{make energy small}} +}{ + \int_{y \in \mathcal{Y}} \underbrace{e^{-\beta E(x, y)}}_{ + \text{make energy big}} +} +\\ +L(Y^*, W) = \underbrace{E_W(Y^*,X)}_{\text{make small}} + \frac{1}{\beta} + \log \int_{y} \underbrace{e^{-\beta E(x, y)}}_{\text{make energy big}} +$$ + +> [!NOTE] +> +> - $\text{gibbs distribution} \rightarrow e^{-\beta E(x, y^*)}$ +> - $\beta$: akin to an inverse temperature + +If you can avoid it, never work with probabilities, as they are often intractable +and give less customization over the scoring function. + +> [!WARNING] +> There may be reasons on why you would prefer having actual probabilities, +> rather than scores, and that's when you need 2 agents that need to interact +> with each other. +> +> Since scores are calibrated only over the model we are working with, values +> across agents will differ, thus meaning different things. +> +> However, this problem does not exist if the agents are trained end-to-end. + +## Contrastive-Methods + +These methods have the objective of **increasing energy for negative +examples and lower it for positive examples** + +Basically we measure the distance, usually with a cosine similarity, and that +becomes our energy function. Then we take a loss function that maximizes over +similarity. + +To make this method work, we need to feed it negative examples as well, otherwise +we would not widen the dissimilarity between positive and negative examples.[^atcold-1] + +> [!NOTE] +> There are also non contrastive methods that only uses positive examples, +> eliminating the need to get negative examples + +## Self Supervised Learning + + + + + +[^stanford-lecun]: [A Tutorial on Energy-Based Learning](https://web.stanford.edu/class/cs379c/archive/2012/suggested_reading_list/documents/LeCunetal06.pdf) + +[^yt-week-7]: [Youtube | Week 7 – Lecture: Energy based models and self-supervised learning | 22nd November 2025](https://www.youtube.com/watch?v=tVwV14YkbYs&t=1411s) + +[^anelli-15]: Vito Walter Anelli | Ch. 15 pg. 17 | 2025-205 + +[^atcold-1]: [DEEP LEARNING | Week 8 Ch. 8.1 | 22nd Novemeber 2025](https://atcold.github.io/NYU-DLSP20/en/week08/08-1/) diff --git 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