From the 2 of 11 linked papers with an AI index.
11 papers
Complementary Matrix-Gated QKAN Fast-Weight Programmers for Quantum Dynamics Forecasting
Kuo-Chung Peng, Samuel Yen-Chi Chen, Jiun-Cheng Jiang +14
The paper proposes Complementary Matrix Gating, a coordinate‑wise gating scheme for fast‑weight programmers built on quantum‑inspired Kolmogorov‑Arnold networks, and shows it impro…
NVAITC AI Scientist: A Governed End-to-End Research System -- A Hypertension GWAS Case Study
Eddie Huang, Ken Liao, Iven Fu +14
The paper introduces NVAITC AI Scientist (NAIS), a governed end‑to‑end AI agent that plans, executes, and oversees biomedical research workflows while keeping protected data within…
Quantum Variational Activation Functions Empower Kolmogorov-Arnold Networks
Jiun-Cheng Jiang, Morris Yu-Chao Huang, Tianlong Chen +1
Variational quantum circuits (VQCs) are central to quantum machine learning, while recent progress in Kolmogorov-Arnold networks (KANs) highlights the power of learnable activation…
Stable Self-Modulating Quantum Fast-Weight Programmers with Bounded Memory Gates
Kuo-Chung Peng, Jiun-Cheng Jiang, Chun-Hua Lin +8
Quantum Fast-Weight Programmers (QFWPs) store temporal information in dynamically programmed variational-circuit parameters rather than in nonlinear recurrent hidden states, offeri…
Parameter-Efficient Quantum-Inspired Fast Weight Programmers for Traffic-Matrix Forecasting
Kuo-Chung Peng, Jiun-Cheng Jiang, Chun-Hua Lin +3
Traffic matrices (TMs) capture network-wide origin-destination demand and are central to traffic engineering, yet accurate whole-matrix forecasting remains challenging when predict…
Self-Modulating Quantum Fast-Weight Programmers for Efficient Adaptive Sequential Learning
Samuel Yen-Chi Chen, Yifeng Peng, Kuo-Chung Peng +8
Recent advances in quantum machine learning have motivated efficient models for sequential data processing. In this paper, we propose Self-Modulating Quantum Fast Weight Programmer…