Shaochen (Henry) Zhong

New pruning type for our ICLR22 paper: Grouped Kernel Pruning — a densely structured pruning granularity with better pruning freedom than filter/channel methods.

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Greetings, We are the authors of the [said paper](https://openreview.net/pdf?id=LdEhiMG9WLO)/[code](https://github.com/choH/lottery_regulated_grouped_kernel_pruning), and we thank you for your inclusion; it has certainly generated some traffic for us. However, it might be worth noting...

It has been a while since HRank published. Let me start off by saying thank you for sharing this interesting piece of work and bringing in a novel perspective in...

VISION of this project.

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To list out the future direction(s) of this project. * **Improving CNN:** * More layers. * Tuning parameters regarding `strides`, `filter`, etc. * **Different model before DQN:** * RNN. *...

* More versatile `cost_graph` output. * Mark trade stamps on time-price graph. Ref. * [Visualizing an Agent’s Thoughts and Actions | Arthur Juliani](https://medium.com/@awjuliani/simple-reinforcement-learning-with-tensorflow-part-5-visualizing-an-agents-thoughts-and-actions-4f27b134bb2a)

Great work with an elegant but effective idea! Thanks for sharing. However, I have a minor suggestion. It is well-known that in the LLM finetuning paradigm, adapter-tuning [1] — done...