diff --git a/README.md b/README.md
index c46f257..eaf402e 100644
--- a/README.md
+++ b/README.md
@@ -51,7 +51,7 @@ pip install -e .
 ## Getting Started
 
 Kronfluence currently supports influence computations on `nn.Linear` and `nn.Conv2d` modules.
-It also supports influence computations with several Hessian approximation techniques: `identity`, `diagonal`, `KFAC`, and `EKFAC`.
+It also supports several other Hessian approximation techniques: `identity`, `diagonal`, `KFAC`, and `EKFAC`.
 The implementation is compatible with [Distributed Data Parallel (DDP)](https://pytorch.org/tutorials/intermediate/ddp_tutorial.html), 
 [Fully Sharded Data Parallel (FSDP)](https://pytorch.org/tutorials/intermediate/FSDP_tutorial.html), and [`torch.compile`](https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html).
 See [DOCUMENTATION.md](https://github.com/pomonam/kronfluence/blob/main/DOCUMENTATION.md) for detailed description how to configure.
@@ -115,9 +115,15 @@ analyzer.compute_pairwise_scores(
 scores = analyzer.load_pairwise_scores(scores_name="pairwise_scoeres")
 ```
 
-## Examples
+## More Examples
 
-(Placeholder for examples)
+The [examples](https://github.com/pomonam/kronfluence/tree/main/examples) folder contains several examples on how to use Kronfluence.
+
+1. MLP & UCI regression datasets
+2. ResNet-9 & CIFAR-10 dataset
+3. ResNet-50 & ImageNet dataset (with DDP).
+4. BERT & GLUE dataset
+5. GPT-2 & WikiText-2 dataset
 
 ## Contributing