diff --git a/content/CSE510/CSE510_L21.md b/content/CSE510/CSE510_L21.md
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+# CSE510 Deep Reinforcement Learning (Lecture 21)
+
+## Exploration in RL
+
+### Information state search
+
+Uncertainty about state transitions or dynamics
+
+Dynamics prediction error or Information gain for dynamics learning
+
+#### Computational Curiosity
+
+- "The direct goal of curiosity and boredom is to improve the world model."
+- "Curiosity Unit": reward is a function of the mismatch between model's current predictions and actuality.
+- There is positive reinforcement whenever the system fails to correctly predict the environment.
+- Thus the usual credit assignment process ... encourages certain past actions in order to repeat situations similar to the mismatch situation. (planning to make your (internal) world model to fail)
+
+#### Reward Prediction Error
+
+- Add exploration reward bonuses that encourage policies to visit states that will cause the prediction model to fail.
+
+$$
+R(s,a,s') = r(s,a,s') + \mathcal{B}(\|T(s,a,\theta)-s'\|)
+$$
+
+- where $r(s,a,s')$ is the extrinsic reward, $T(s,a,\theta)$ is the predicted next state, and $\mathcal{B}$ is a bonus function (intrinsic reward bonus).
+- Exploration reward bonuses are non-stationary: as the agent interacts with the environment, what is now new and novel, becomes old and known.
+
+[link to the paper](https://arxiv.org/pdf/1507.08750)
+</details>
+
+<details>
+<summary>Example</summary>
+
+Learning Visual Dynamics
+
+- Exploration reward bonuses $\mathcal{B}(s, a, s') = \|T(s, a; \theta) - s'\|$
+  - However, trivial solution exists: could get reward by just moving around randomly.
+
+---
+
+- Exploration reward bonuses with autoencoders $\mathcal{B}(s, a, s') = \|T(E(s';\theta),a;\theta)-E(s';\theta)\|$
+  - But suffer the problems of autoencoding reconstruction loss that has little to do with our task
+
+#### Task Rewards vs. Exploration Rewards
+
+Exploration rewards bonuses:
+
+$$
+\mathcal{B}(s, a, s') = \|T(E(s';\theta),a;\theta)-E(s';\theta)\|
+$$
+
+Only task rewards:
+
+$$
+R(s,a,s') = r(s,a,s')
+$$
+
+Task+curiosity rewards:
+
+$$
+R^t(s,a,s') = r(s,a,s') + \mathcal{B}^t(s, a, s')
+$$
+
+Sparse task + curiosity rewards:
+
+$$
+R^t(s,a,s') = r^t(s,a,s') + \mathcal{B}^t(s, a, s')
+$$
+
+Only curiosity rewards:
+
+$$
+R^c(s,a,s') = \mathcal{B}^c(s, a, s')
+$$
+
+#### Extrinsic reward RL is not New
+
+- Itti, L., Baldi, P.F.: Bayesian surprise attracts human attention. In: NIPS’05. pp. 547–554 (2006)
+- Schmidhuber, J.: Curious model-building control systems. In: IJCNN’91. vol. 2, pp. 1458–1463 (1991)
+- Schmidhuber, J.: Formal theory of creativity, fun, and intrinsic motivation (1990-2010). Autonomous Mental Development, IEEE Trans. on Autonomous Mental Development 2(3), 230–247 (9 2010)
+- Singh, S., Barto, A., Chentanez, N.: Intrinsically motivated reinforcement learning. In: NIPS’04 (2004)
+- Storck, J., Hochreiter, S., Schmidhuber, J.: Reinforcement driven information acquisition in non-deterministic environments. In: ICANN’95 (1995)
+- Sun, Y., Gomez, F.J., Schmidhuber, J.: Planning to be surprised: Optimal Bayesian exploration in dynamic environments (2011), http://arxiv.org/abs/1103.5708
+
+#### Limitation of Prediction Errors
+
+- Agent will be rewarded even though the model cannot improve.
+- So it will focus on parts of environment that are inherently unpredictable or stochastic.
+- Example: the noisy-TV problem
+  - The agent is attracted forever in the most noisy states, with unpredictable outcomes.
+
+#### Random Network Distillation
+
+Original idea: Predicting the output of a fixed and randomly initialized neural network on the next state, given the current state and action.
+
+New idea: Predicting the output of a fixed and randomly initialized neural network on the next state, given the **next state itself.**
+
+- The target network is a neural network with fixed, randomized weights, which is never trained.
+- The prediction network is trained to predict the target network's output.
+
+> the more you visit the state, the less loss you will have.
+
+### Posterior Sampling
+
+Uncertainty about Q-value functions or policies
+
+Selecting actions according to the probability they are best according to the current model.
+
+#### Exploration with Action Value Information
+
+Count-Based and Curiosity-driven method does not take into
+account the action value information
+
+![Action Value Information](https://notenextra.trance-0.com/CSE510/Action_Value_Information.png)
+
+> In this case, the optimal solution is action 1, but we will explore action 3 because it has the highest uncertainty. And it takes long to distinguish action 1 and 2 since they have similar values.
+
+#### Exploration via Posterior Sampling of Q Functions
+
+- Represent a posterior distribution of Q functions, instead of a point estimate.
+  1. Sample from $P(Q), Q\sim P(Q)$
+  2. Choose actions according to this $Q$ for one episode $a=\arg\max_{a} Q(s,a)$
+  3. Update $P(Q)$ based on the sampled $Q$ and collected experience tuples $(s,a,r,s')$
+- Then we do not need $\epsilon$-greedy for exploration! Better exploration by representing uncertainty over Q.
+- But how can we learn a distribution of Q functions $P(Q)$ if Q function is a deep neural network?
+
+#### Bootstrap Ensemble
+
+- Neural network ensembles: train multiple Q-function approximations each on using different subset of the data
+  - Computationally expensive
+- Neural network ensembles with shared backbone: only the heads are trained with different subset of the data
+
+### Questions
+
+- Why do PG methods implicitly support exploration?
+- Is it sufficient? How can we improve its implicit exploration?
+- What are limitations of entropy regularization?
+- How can we improve exploration for PG methods?
+  - Intrinsic-motivated bonuses (e.g., RND)
+  - Explicitly optimize per-state entropy in the return (e.g., SAC)
+  - Hierarchical RL
+  - Goal-conditional RL
+- What are potentially more effective exploration methods?
+  - Knowledge-driven
+  - Model-based exploration
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diff --git a/content/CSE510/_meta.js b/content/CSE510/_meta.js
index 43bbfc8..780b495 100644
--- a/content/CSE510/_meta.js
+++ b/content/CSE510/_meta.js
@@ -23,4 +23,5 @@ export default {
     CSE510_L18: "CSE510 Deep Reinforcement Learning (Lecture 18)",
     CSE510_L19: "CSE510 Deep Reinforcement Learning (Lecture 19)",
     CSE510_L20: "CSE510 Deep Reinforcement Learning (Lecture 20)",
+    CSE510_L21: "CSE510 Deep Reinforcement Learning (Lecture 21)",
 }
\ No newline at end of file
diff --git a/content/CSE5313/CSE5313_L19.md b/content/CSE5313/CSE5313_L19.md
index 98ba394..2764b2a 100644
--- a/content/CSE5313/CSE5313_L19.md
+++ b/content/CSE5313/CSE5313_L19.md
@@ -230,3 +230,4 @@ $$
 > [!TIP]
 >
 > error + known location $\implies$ erasure. $d = 2 \implies$ 1 erasure is correctable.
+
diff --git a/content/CSE5313/CSE5313_L20.md b/content/CSE5313/CSE5313_L20.md
new file mode 100644
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+++ b/content/CSE5313/CSE5313_L20.md
@@ -0,0 +1,252 @@
+# CSE5313 Coding and information theory for data science (Lecture 20)
+
+## Review for Private Information Retrieval
+
+### PIR from replicated databases
+
+For 2 replicated databases, we have the following protocol:
+
+- User has $i \sim U_{m}$.
+- User chooses $r_1, r_2 \sim U_{\mathbb{F}_2^m}$.
+- Two queries to each server:
+  - $q_{1, 1} = r_1 + e_i$, $q_{1, 2} = r_2$.
+  - $q_{2, 1} = r_1$, $q_{2, 2} = r_2 + e_i$.
+- Server $j$ responds with $q_{j, 1} c_j^\top$ and $q_{j, 2} c_j^\top$.
+- Decoding?
+  - $q_{1, 1} c_1^\top + q_{2, 1} c_2^\top = r_1 c_1 + c_2 + e_i c_1^\top = r_1 \cdot 0^\top + x_{i, 1} = x_{i, 1}$.
+  - $q_{1, 2} c_1^\top + q_{2, 2} c_2^\top = r_2 c_1 + c_2 + e_i c_2^\top = x_{i, 2}$.
+
+PIR-rate is $\frac{k}{2k} = \frac{1}{2}$.
+
+### PIR from coded parity-check databases
+
+For 3 coded parity-check databases, we have the following protocol:
+
+- User has $i \sim U_{m}$.
+- User chooses $r_1, r_2, r_3 \sim U_{\mathbb{F}_2^m}$.
+- Three queries to each server:
+  - $q_{1, 1} = r_1 + e_i$, $q_{1, 2} = r_2$, $q_{1, 3} = r_3$.
+  - $q_{2, 1} = r_1$, $q_{2, 2} = r_2 + e_i$, $q_{2, 3} = r_3$.
+  - $q_{3, 1} = r_1$, $q_{3, 2} = r_2$, $q_{3, 3} = r_3 + e_i$.
+- Server $j$ responds with $q_{j, 1} c_j^\top, q_{j, 2} c_j^\top, q_{j, 3} c_j^\top$.
+- Decoding?
+  - $q_{1, 1} c_1^\top + q_{2, 1} c_2^\top + q_{3, 1} c_3^\top = r_1 c_1 + c_2 + c_3 + e_i c_1^\top = r_1 \cdot 0^\top + x_{i, 1} = x_{i, 1}$.
+  - $q_{1, 2} c_1^\top + q_{2, 2} c_2^\top + q_{3, 2} c_3^\top = r_2 c_1 + c_2 + c_3 + e_i c_2^\top = x_{i, 2}$.
+  - $q_{1, 3} c_1^\top + q_{2, 3} c_2^\top + q_{3, 3} c_3^\top = r_3 c_1 + c_2 + c_3 + e_i c_3^\top = x_{i, 3}$.
+
+PIR-rate is $\frac{k}{3k} = \frac{1}{3}$.
+
+## Beyond z=1
+
+### Star-product theme
+
+Given $x=(x_1, \ldots, x_j)_{j\in [n]}, y=(y_1, \ldots, y_j)_{j\in [n]}$, over $\mathbb{F}_q$, the star-product is defined as:
+
+$$
+x \star y = (x_1 y_1, \ldots, x_n y_n)
+$$
+
+Given two linear codes, $C,D\subseteq \mathbb{F}_q^n$, the star-product code is defined as:
+
+$$
+C \star D = span_{\mathbb{F}_q} \{x \star y | x \in C, y \in D\}
+$$
+
+Singleton bound for star-product:
+
+$$
+d_{C \star D} \leq n-\dim C-\dim D+2
+$$
+
+### PIR form a database coded with any MDS code and z>1
+
+To generalize the previous scheme to $z > 1$ need to encode multiple $r$'s together.
+
+- As in the ramp scheme.
+
+> Recall from the ramp scheme, we use $r_1, \ldots, r_z \sim U_{\mathbb{F}_q^k}$ as our key vector to avoid occlusion of the servers.
+
+In the star-product scheme:
+
+- Files are coded with an MDS code $C$.
+- The multiple $r$'s are coded with an MDS code $D$.
+- The scheme is based on the minimum distance of $C \star D$.
+
+To code the data:
+
+- Let $C \subseteq \mathbb{F}_q^n$ be an MDS code of dimension $k$.
+- For all $j \in m$, encode file $x_j = x_{j, 1}, \ldots, x_{j, k}$ using $G_C$:
+
+$$
+\begin{pmatrix}
+x_{1, 1} & x_{1, 2} & \cdots & x_{1, k}\\
+x_{2, 1} & x_{2, 2} & \cdots & x_{2, k}\\
+\vdots & \vdots & \ddots & \vdots\\
+x_{m, 1} & x_{m, 2} & \cdots & x_{m, k}
+\end{pmatrix} \cdot G_C = \begin{pmatrix}
+c_{1, 1} & c_{1, 2} & \cdots & c_{1, n}\\
+c_{2, 1} & c_{2, 2} & \cdots & c_{2, n}\\
+\vdots & \vdots & \ddots & \vdots\\
+c_{m, 1} & c_{m, 2} & \cdots & c_{m, n}
+\end{pmatrix}
+$$
+
+- For all $j \in n$, store $c_j = c_{1, j}, c_{2, j}, \ldots, c_{m, j}$ (a column of the above matrix) in server $j$.
+
+Let $r_1, \ldots, r_z \sim U_{\mathbb{F}_q^k}$.
+
+To code the queries:
+
+- Let $D \subseteq \mathbb{F}_q^k$ be an MDS code of dimension $z$.
+- Encode the $r_j$'s using $G_D=[g_1^\top, \ldots, g_z^\top]$.
+
+$$
+(r_1^\top, \ldots, r_z^\top) \cdot G_D = \begin{pmatrix}
+r_{1, 1} & r_{2, 1} & \cdots & r_{z, 1}\\
+r_{1, 2} & r_{2, 2} & \cdots & r_{z, 2}\\
+\vdots & \vdots & \ddots & \vdots\\
+r_{1, m} & r_{2, m} & \cdots & r_{z, m}
+\end{pmatrix}
+\cdot G_D=\left((r_1^\top,\ldots, r_z^\top)g_1^\top,\ldots, (r_1^\top,\ldots, r_z^\top)g_n^\top \right)
+$$
+
+To introduce the "errors in known locations" to the encoded $r_j$'s:
+
+- Let $W \in \{0, 1\}^{m \times n}$ with some $d_{C \star D} - 1$ entries in its $i$-th row equal to 1.
+- These are the entries we will retrieve.
+
+For every server $j \in [n]$ send $q_j = r_1^\top, \ldots, r_z^\top g_j^\top + w_j$, where $w_j$ is the $i$-th column of $W$.
+
+- This is similar to ramp scheme, where $w_j$ is the "message".
+- Privacy against collusion of $z$ servers.
+
+Response from server: $a_j = q_j c_j^\top$.
+
+Decoding? Let $Q \in \mathbb{F}_q^{m \times n}$ be a matrix whose columns are the $q_j$'s.
+$$
+Q = \begin{pmatrix}
+r_1^\top & \cdots & r_z^\top
+\end{pmatrix} \cdot G_D + W
+$$
+
+- The user has
+
+$$
+\begin{aligned}
+q_1 c_1^\top, \ldots, q_n c_n^\top &= \left(\sum_{j \in m} q_{1, j} c_{j, 1}, \ldots, \sum_{j \in m} q_{n, j} c_{j, n}\right) \\
+&=\sum_{j \in m} (q_{1,j}c_{j, 1}, \ldots, q_{n,j}c_{j, n}) \\
+&=\sum_{j \in m} q^j \star c^j
+$$
+
+where $q^j$ is a row of $Q$ and $c^j$ is a codeword in $C$ (an $n, k$ $q$ MDS code).
+
+We have:
+
+- $Q=(r_1^\top, \ldots, r_z^\top) \cdot G_D + W$
+- $W\in \{0, 1\}^{m \times n}$ with some $d_{C \star D} - 1$ entries in its $i$-th row equal to 1.
+- $(q^j \star c^j)=sum_{j \in m} q^j \star c^j$
+- Each $q^j$ is a row of $Q$
+  - For $j \neq i$, $q^j$ is a codeword in $D$
+  - $q^i = d^i + w^i$
+- Therefore:
+
+$$
+\begin{aligned}
+\sum_{j \in [m]} q^j \star c^j &= \sum_{j \neq i} (d^j \star c^j) + ((d^i + w^i) \star c^i) \\
+&= \sum_{j \neq i} (d^j \star c^j) + w^i \star c^i
+&= (\text{codeword in } C \star D )+( \text{noise of Hamming weight } \leq d_{C \star D} - 1)
+\end{aligned}
+$$
+
+Multiply by $H_{C \star D}$ and get $d_{C \star D} - 1$ elements of $c^i$.
+
+- Recall that $c^i = x_i \cdot G_C$
+- Repeat $k^{d_{C \star D} - 1}$ times to obtain $k$ elements of $c^i$.
+  - Suffices to obtain $x_i$, since $C$ is $n, k$ $q$ MDS code.
+
+PIR-rate:
+
+- = $\frac{k}{# \text{ downloaded elements}} = \frac{k}{\frac{k}{d_{C \star D} - 1} \cdot n} = \frac{d_{C \star D} - 1}{n}$
+- Singleton bound for star-product: $d_{C \star D} \leq n - \dim C - \dim D + 2$.
+- Achieved with equality if $C$ and $D$ are Reed-Solomon codes.
+- PIR-rate = $\frac{n - \dim C - \dim D + 1}{n} = \frac{n - k - z + 1}{n}$.
+- Intuition:
+  - "paying" $k$ for "reconstruction from any $k$".
+  - "paying" $z$ for "protection against colluding sets of size $z$".
+- Capacity unknown! (as of 2022).
+  - Known for special cases, e.g., $k = 1, z = 1$, certain types of schemes, etc.
+
+### PIR over graphs
+
+Graph-based replication:
+
+- Every file is replicated twice on two separate servers.
+- Every two servers have at most one file in common.
+- "file" = "granularity" of data, i.e., the smallest information unit shared by any two servers.
+
+A server that stores $(x_{i, j})_{j=1}^d$ receives $(q_{i, j})_{j=1}^d$, and replies with $\sum_{j=1}^d q_{i, j} \cdot x_{i, j}$.
+
+The idea:
+
+- Consider a 2-server replicated PIR and "split" the queries between the servers.
+- Sum the responses, unwanted files "cancel out", while $x_i$ does not.
+
+Problem: Collusion.
+
+Solution: Add per server randomness.
+
+Good for any graph, and any $q \geq 3$ (for simplicity assume $2 | q$).
+
+The protocol:
+
+- Choose random $\gamma \in \mathbb{F}_q^n$, $\nu \in \mathbb{F}_q^m$, and $h \in \mathbb{F} \setminus \{0, 1\}$.
+- Queries:
+  - If node $j$ is incident with edge $\ell$, send $q_{j, \ell} = \gamma_j \cdot \nu_\ell$ to node $j$.
+  - I.e., if server $j$ stores file $\ell$.
+- Except one node $j_0$ that stores $x_i$, which gets $q_{j_0, i} = h \cdot \gamma_{j_0} \cdot \nu_i$.
+- Server $j$ responds with $a_j = \sum_{j=1}^d q_{j, \ell} \cdot x_{i, \ell}$.
+  - Where $x_{i, 1}, \ldots, $x_{i, d}$ are the files adjacent with it.
+
+<details>
+<summary>Example</summary>
+
+- Consider the following graph.
+- $n = 5, m = 7, and i = 3$.
+- $q_3 = \gamma_3 \cdot v_2, v_3, v_6$ and $a_3 = x_2 \cdot \gamma_3 v_2 + x_3 \cdot \gamma_3 v_3 + x_6 \cdot \gamma_3 v_6$.
+- $q_2 = \gamma_2 \cdot v_1, h v_3, v_4$ and $a_2 = x_1 \cdot \gamma_2 v_1 + x_3 \cdot h \gamma_2 v_3 + x_4 \cdot \gamma_2 v_4$.
+
+![Example of PIR over graphs](https://notenextra.trance-0.com/CSE5313/PIR_over_graphs.png)
+
+</details>
+
+Correctness:
+
+- $\sum_{j=1}^5 \gamma_j^{-1} a_j =( h + 1 )v_3 x_3$
+- $h \neq 1, v_3 \neq 0 \implies$ find $x_3$.
+
+Parameters:
+
+- Storage overhead 2 (for any graph).
+- Download $n \cdot k$.
+- PIR rate 1/n.
+
+Collusion resistance:
+
+1-privacy: Each node sees an entirely random vector.
+
+2-privacy:
+
+- If no edge – as for 1-privacy.
+- If edge exists – E.g.,
+  - $\gamma_3 v_6$ and $\gamma_4 v_6$ are independent.
+  - $\gamma_3 v_3$ and $h \cdot \gamma_2 v_3$ are independent.
+
+S-privacy: 
+
+- Let $S \subseteq n$ (e.g., $S = 2,3,5$), and consider the query matrix of their mutual files: 
+
+$$
+Q_S = diag(\gamma_3, \gamma_2, \gamma_5) \begin{pmatrix} 1 &\\ h & 1 \\ & 1\end{pmatrix} diag(v_3, v_4)
+$$
+
+- It can be shown that $Pr(Q_S)=\frac{1}{(q-1)^4}$, regardless of $i \implies$ perfect privacy.
diff --git a/content/CSE5313/_meta.js b/content/CSE5313/_meta.js
index dd6b483..bcc1380 100644
--- a/content/CSE5313/_meta.js
+++ b/content/CSE5313/_meta.js
@@ -22,5 +22,6 @@ export default {
     CSE5313_L16: "CSE5313 Coding and information theory for data science (Exam Review)",
     CSE5313_L17: "CSE5313 Coding and information theory for data science (Lecture 17)",
     CSE5313_L18: "CSE5313 Coding and information theory for data science (Lecture 18)",
-    CSE5313_L19: "CSE5313 Coding and information theory for data science (Exam Review)",
+    CSE5313_L19: "CSE5313 Coding and information theory for data science (Lecture 19)",
+    CSE5313_L20: "CSE5313 Coding and information theory for data science (Lecture 20)",
 }
\ No newline at end of file
diff --git a/content/CSE5519/CSE5519_C4.md b/content/CSE5519/CSE5519_C4.md
index 6177990..2422611 100644
--- a/content/CSE5519/CSE5519_C4.md
+++ b/content/CSE5519/CSE5519_C4.md
@@ -1,2 +1,13 @@
 # CSE5519 Advances in Computer Vision (Topic C: 2024 - 2025: Neural Rendering)
 
+## COLMAP-Free 3D Gaussian SplattingLinks to an external site
+
+[link to the paper](https://arxiv.org/pdf/2312.07504)
+
+We propose a novel 3D Gaussian Splatting (3DGS) framework that eliminates the need for COLMAP for camera pose estimation and bundle adjustment.
+
+> [!TIP]
+>
+> This paper presents a novel 3D Gaussian Splatting framework that eliminates the need for COLMAP for camera pose estimation and bundle adjustment.
+> 
+> Inspired by point map construction, the author uses Gaussian splatting to reconstruct the 3D scene. I wonder how this method might contribute to higher resolution reconstruction or improvements. Can we use the original COLMAP on traditional NeRF methods for comparable results?
diff --git a/content/CSE5519/CSE5519_F5.md b/content/CSE5519/CSE5519_F5.md
index d77dc4d..bcb9191 100644
--- a/content/CSE5519/CSE5519_F5.md
+++ b/content/CSE5519/CSE5519_F5.md
@@ -1,2 +1,17 @@
 # CSE5519 Advances in Computer Vision (Topic F: 2025: Representation Learning)
 
+## Can Generative Models Improve Self-Supervised Representation Learning?
+
+[link to the paper](https://arxiv.org/pdf/2403.05966)
+
+### Novelty in SSL with Generative Models
+
+- Use generative models to generate synthetic data to train self-supervised representation learning models.
+- Use generative augmentation to generate new data from the original data using a generative model. (with gaussian noise, or other data augmentation techniques)
+- Using standard augmentation techniques like flipping, cropping, and color jittering with generative techniques can further improve the performance of the self-supervised representation learning models.
+
+> [!TIP]
+>
+> This paper shows that using generative models to generate synthetic data can improve the performance of self-supervised representation learning models. The key seems to be the use of generative augmentation to generate new data from the original data using a generative model.
+> 
+> However, both representation learning and generative modeling have some hallucinations. I wonder will these kinds of hallucinations would be reinforced, and the bias in the generation model would propagate to the representation learning model in the process of generative augmentation?
diff --git a/public/CSE510/Action-value-information.png b/public/CSE510/Action-value-information.png
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