arXiv:2607. 26946v1 Announce Type: new Abstract: Recent advancements in Computer Go, driven by AlphaZero and MuZero, rely heavily on Monte Carlo Tree Search (MCTS) to correct the errors of the neural network policy.
By Mehrad Yaghoubi, Azam Bastanfard, Abbas Jalilvand, Ashkan Rezaei
arXiv:2608.06912v2 Announce Type: replace
Abstract: Selecting the top-$k$ elements is a fundamental operation for inducing sparsity in large-scale models and optimization problems, enabling robust ex...
By Jakub Antczak, Joanna Wojciechowicz, Kamil Ksi\k{a}\.zek, Marcin Mazur, {\L}ukasz Struski, Jacek Tabor
arXiv:2608.20497v1 Announce Type: new
Abstract: Deploying high-accuracy neural networks on resource-constrained edge devices remains challenging, as existing approaches treat training, compression, a...
By Malak Gamal El-Din, Yifan Zhang, Yasser Shoukry, Sitao Huang, Salma Elmalaki
Boosted decision trees (BDTs) are widely used in latency-critical applications, but efficient hardware deployment remains challenging. Existing designs often rely on uniform or manually tuned fixed-point formats, which can introduce unnecessary hardware cost or accuracy loss.
The paper presents a PyTorch-based framework for designing and optimizing binarized neural networks, incorporating freezing and pruning mechanisms. It introduces a novel pruning method that uses a global weighting scheme to assess parameter importance across abstraction levels, achieving a 70% pruning rate on VGG11 without sacrificing accuracy—outperforming existing binarized pruning results of 41%. The framework facilitates rapid, reproducible evaluation and prototyping of state‑of‑the‑art binarized network techniques.
By Roan Rubiales, Jean Pierre David
arXiv:2607. 15328v1 Announce Type: cross Abstract: Complex algorithms such as deep neural networks are increasingly being deployed on embedded, resource constrained platforms.
By Taisa Kushner (Galois Inc), Ryan McCleeary (Galois Inc), Martin Brain (City St George University of London)