June Liu Zia Work [verified] Page

+--------------------------------------------------------+ | Dense Deep Learning Model | | (High Latency / High Footprint) | +---------------------------+----------------------------+ | ======================================= | Optimization Framework Pillars | ======================================= | +----------------------+----------------------+ | | | v v v +------------+ +--------------+ +-------------+ | Pruning | | Quantization | | Distillation| | (Removes | | (Reduces bit | | (Transfers | | redundant | | precision, | | knowledge to| | channels) | | e.g. FP32->INT8) | small model)| +------------+ +--------------+ +-------------+ | | | +----------------------+----------------------+ | v +--------------------------------------------------------+ | Optimized Deep Learning Model | | (Low Latency / Edge Deployable) | +--------------------------------------------------------+ 1. Structured vs. Unstructured Network Pruning

. However, several individuals named June (or Jun) Liu have made significant contributions across different fields.

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Conversely, traditionalists argue that her mixed-media approach dilutes both mediums. A review in Hyperallergic stated: "June Liu Zia work often feels like a brilliant thesis statement in search of a coherent novel. The concept is always heavier than the execution."

focuses on and how its dysregulation leads to diseases, using biochemistry and advanced imaging. Unstructured Network Pruning

She recently told The Brooklyn Rail : “I don’t want my work to resolve. Resolution is a lie. I want it to ache a little, like a healing bruise.”

The primary, and perhaps most recognizable, theme in Ziqian Liu’s portfolio is the harmonious co-existence between humans and the natural world. Her work is deeply rooted in the belief that human beings are equal to other living creatures, coexisting within the same ecosystem and sharing the same atmosphere. This link or copies made by others cannot be deleted

Liu constructs, rather than just captures, images where plants and the human body exist in delicate balance, showcasing beauty in this symbiotic state.

Modern deep learning architectures, such as deep convolutional neural networks (CNNs) and transformer models, boast billions of parameters. While these dense parameter structures allow models to learn intricate data patterns, they present major operational challenges: