Titlebook: Neural Information Processing; 30th International C Biao Luo,Long Cheng,Chaojie Li Conference proceedings 2024 The Editor(s) (if applicable

步履蹒跚

Yan Li,Jiazhu Huang,Yuezu Lv,Jialing Zhouoving the product line approach in your organization.Benefit.Software product lines represent perhaps the most exciting paradigm shift in software development since the advent of high-level programming languages. Nowhere else in software engineering have we seen such breathtaking improvements in cos

HUMP

Jiazhu Huang,Yan Li,Yuezu Lvs. Nowhere else in software engineering have we seen such breathtaking improvements in cost, quality, time to market, and developer productivity, often registering in the order-of-magnitude range. While the underlying concepts are straightforward enough – building a family of related products or sys

Judicious

Single Feedback Based Kernel Generalized Maximum Correntropy Adaptive Filtering Algorithmamework of kernel adaptive filtering. In SF-KGMC, the history information implicitly existing in the single delayed output can enhance the convergence rate. Compared to the second-order statistics criterion, the generalized maximum correntropy (GMC) criterion shows better robustness against outliers

micronized

未成熟

Learning Adaptable Risk-Sensitive Policies to Coordinate in Multi-agent General-Sum Gamested stag hunt (ISH). Previous works address this challenge by sharing rewards or shaping their opponents’ learning process, which require too strong assumptions. In this paper, we observe that agents trained to optimize expected returns are more likely to choose a safe action that leads to guarantee

Forehead-Lift

清楚说话

ADEQ: Adaptive Diversity Enhancement for Zero-Shot Quantizationnd security issues. Most existing synthetic-data-driven zero-shot quantization methods introduce diversity enhancement to simulate the distribution of real samples. However, the adaptivity between the enhancement degree and network is neglected, i.e., whether the enhancement degree benefits differen

高度

领袖气质

Amortized Variational Inference via Nosé-Hoover Thermostat Hamiltonian Monte Carlo (MCMC) is such a useful tool to do that but at the cost of computational burden since it needs many transition steps to converge to the stationary distribution for each datapoint. Amortized variational inference within the framework of MCMC is thus proposed where the learned parameters of the model

Charade

AM-RRT*: An Automatic Robot Motion Planning Algorithm Based on RRTts. Focusing on the shortcomings of traditional RRT methods such as long, unsmooth paths, and uncoupling with robot control system, an automatic robot motion planning method was proposed based on Rapid Exploring Random Tree called AM-RRT* (automatic motion planning based on RRT*). First, the RRT alg