Update README.md

README.md

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 # Model Card for Model ID
 
-Based on a class of partial differential equations called **quasi-linear hyperbolic systems** [[Liu et al, 2023](https://github.com/liuyao12/ConvNets-PDE-perspective)], the **QLNet** breaks into uncharted waters of ConvNet model space marked by the use of (element-wise) multiplication in lieu of ReLU as the primary nonlinearity. It achieves comparable performance as ResNet50 on ImageNet-1k (acc=**78.61**), demonstrating that it has the same level of capacity/expressivity, and deserves more analysis and study (hyper-paremeter tuning, optimizer, etc.) by the academic community.
+Based on a class of partial differential equations called **quasi-linear hyperbolic systems** [[Liu et al, 2023](https://github.com/liuyao12/ConvNets-PDE-perspective)], the **QLNet** breaks into uncharted waters of ConvNet model space marked by the use of (element-wise) multiplication in lieu of ReLU as the primary nonlinearity. It achieves comparable performance as ResNet50 on ImageNet-1k (acc=**78.61**), demonstrating that it has the same level of capacity/expressivity, and deserves further analysis and study (hyper-paremeter tuning, optimizer, etc.) by the academic community.
 
-The overall architecture folllows that of the origianl ConvNet (LeCun) and ResNet (He et al.), with the use of "depthwise" as in MobileNet.
+The overall architecture follows that of the origianl ConvNet (LeCun) and ResNet (He et al.), with the adoption of "depthwise convolution" as in MobileNet.
 
 ![](https://huggingface.co/liuyao/QLNet/resolve/main/PDE_perspective.jpeg)
 
-One notable feature is that the architecture (trained or not) admits a *continuous* symmetry in its parameters. Check out the [notebook](https://colab.research.google.com/#fileId=https://huggingface.co/liuyao/QLNet/blob/main/QLNet_symmetry.ipynb) for a demo that makes a particular transformation on the weights while leaving the output *unchanged*.
+One notable feature is that the architecture (trained or not) admits a *continuous* symmetry in its parameters. Check out the [notebook](https://colab.research.google.com/#fileId=https://huggingface.co/liuyao/QLNet/blob/main/QLNet_symmetry.ipynb) for a demo that makes a transformation on the weights while leaving the output *unchanged*.
 
 FAQ (as the author imagines):
 
 - **Q**: *Who needs another ConvNet, when the SOTA for ImageNet-1k is now in the low 80s with models of comparable size?*
-- **A**: Aside from lack of resources to perform extensive experiments, the real answer is that the new symmetry has the potential to be exploited (e.g., symmetry-aware optimization). The non-activation nonlinearity does have more "naturalness" (coordinate independence) that is innate in many equations in mathematics and physics. Activation is but a legacy from the early days of models inspired by *biological* neural networks.
+- **A**: Aside from a lack of resources to perform extensive experiments, the real answer is that the new symmetry has the potential to be exploited (as a kind of symmetry-aware optimization). The "non-activation" nonlinearity is more "natural" (coordinate independence) as in many equations in mathematics and physics. Activation (of neurons or units) is but a legacy from the early days of models inspired by *biological* neural networks.
 - **Q**: *Multiplication is too simple, someone must have tried it?*
 - **A**: Perhaps. My guess is whoever tried it soon found the model fail to train with standard ReLU. Without the conviction in the underlying PDE perspective, maybe it wasn't pushed to its limit.
 - **Q**: *Is it not similar to attention in Transformer?*
-- **A**: It is, indeed. It's natural to wonder if the activation functions in Transformer could be removed (or reduced) while still achieve comparable performance.
+- **A**: Yes, indeed. It's natural to wonder if the activation functions in Transformer could be removed (or reduced) while still achieve comparable performance.
 - **Q**: *If the parameter space has a symmetry (beyond permutations), perhaps there's redundancy in the weights.*
 - **A**: The transformation in our demo indeed can be used to reduce the weights from the get-go. However, there are variants of the model that admit a much larger symmetry. It is also related to the phenomenon of "flat minima" found empirically in some conventional neural networks.