H3DNAS is a hardware‑aware compression framework that operates directly on ONNX computational graphs, eliminating the need for original source code or gradient access. It introduces a Channel Dependency Graph to classify operators and compute a provable compression ceiling, and employs a two‑stage hierarchical search that prunes architectures via L1‑importance channel selection and applies GhostConv mutations to Pareto‑optimal candidates. Applied to 3D point‑cloud models on the ModelNet40 dataset, H3DNAS reduces parameters by up to 65.5% and achieves significant inference speedups with negligible accuracy loss.
By Anchit Mulye, Rhythm Baghel, Sujay Kumar Ingle, Hardik Jain
arXiv:2606. 09924v1 Announce Type: cross Abstract: Deploying deep neural networks on memory-constrained edge accelerators is bottlenecked by per-inference off-chip weight transfer rather than computation: the dense network cannot be retained on-chip, and every parameter must be loaded for every input.
By Kohga Tanaka, Hiroaki Nishi
The paper introduces Neural Spectral Capacity (NSC), a closed‑form metric derived from the singular‑value spectrum of weight matrices that can be computed solely from a network’s architectural specification. Unlike traditional measures such as #Params and #FLOPs, NSC captures architectural structure (depth, width, head, FFN allocations) and can be evaluated without instantiating the model, data, or gradients. Using a dynamic‑programming solver (NSC‑DP), the authors demonstrate that NSC can efficiently identify architectures that outperform existing training‑free proxies across Transformer and CNN families, and achieve state‑of‑the‑art results in tasks such as WikiText‑103 and commonsense reasoning with LLaMA‑7B.
whyItMatters":"NSC provides a fast, architecture‑only proxy that outperforms conventional metrics and training‑free proxies, enabling more effective design and pruning of large models without costly training or data."
By Chenyu Zhu, Ruoyu Zhao, Zhichao Lu
arXiv:2509. 08685v2 Announce Type: replace-cross Abstract: Given encoded 3D point cloud geometry available at the decoder, we study the problem of lossy attribute compression in a multi-resolution B-spline projection framework.
By Tam Thuc Do, Philip A. Chou, Gene Cheung
arXiv:2601. 16622v2 Announce Type: replace-cross Abstract: Equivariant Graph Neural Networks (EGNNs) have become a widely used approach for modeling 3D atomistic systems.
By Lin Huang, Chengxiang Huang, Ziang Wang, Yiyue Du, Chu Wang, Haocheng Lu, Yunyang Li, Xiaoli Liu, Arthur Jiang, Jia Zhang
arXiv:2608. 00859v1 Announce Type: new Abstract: Kolmogorov--Arnold Networks (KANs) replace scalar edge weights with learnable univariate functions parameterized by multiple basis coefficients.
By Kazi Ahmed Asif Fuad, Lizhong Chen