Search Results for author:
Found 18 papers, 5 papers with code
D$^2$-DPM: Dual Denoising for Quantized Diffusion Probabilistic Models
In this work, we propose D2-DPM, a dual denoising mechanism aimed at precisely mitigating the adverse effects of quantization noise on the noise estimation network.
Training-free Heterogeneous Model Merging
To overcome this challenge, we propose an innovative model merging framework designed for heterogeneous models, encompassing both depth and width heterogeneity.
Multi-agent Path Finding for Mixed Autonomy Traffic Coordination
In the evolving landscape of urban mobility, the prospective integration of Connected and Automated Vehicles (CAVs) with Human-Driven Vehicles (HDVs) presents a complex array of challenges and opportunities for autonomous driving systems.
ITINERA: Integrating Spatial Optimization with Large Language Models for Open-domain Urban Itinerary Planning
Citywalk, a recently popular form of urban travel, requires genuine personalization and understanding of fine-grained requests compared to traditional itinerary planning.
Multi-agent Path Finding for Cooperative Autonomous Driving
Anticipating possible future deployment of connected and automated vehicles (CAVs), cooperative autonomous driving at intersections has been studied by many works in control theory and intelligent transportation across decades.
Adaptive Policy Learning for Offline-to-Online Reinforcement Learning
In this paper, we consider an offline-to-online setting where the agent is first learned from the offline dataset and then trained online, and propose a framework called Adaptive Policy Learning for effectively taking advantage of offline and online data.
SnCQA: A hardware-efficient equivariant quantum convolutional circuit architecture
We propose SnCQA, a set of hardware-efficient variational circuits of equivariant quantum convolutional circuits respective to permutation symmetries and spatial lattice symmetries with the number of qubits $n$.
Unifying O(3) Equivariant Neural Networks Design with Tensor-Network Formalism
Equivariant graph neural networks are a standard approach to such problems, with one of the most successful methods employing tensor products between various tensors that transform under the spatial group.
Quantum Power Flows: From Theory to Practice
Climate change is becoming one of the greatest challenges to the sustainable development of modern society.
On the Super-exponential Quantum Speedup of Equivariant Quantum Machine Learning Algorithms with SU($d$) Symmetry
We introduce a framework of the equivariant convolutional algorithms which is tailored for a number of machine-learning tasks on physical systems with arbitrary SU($d$) symmetries.
Speeding up Learning Quantum States through Group Equivariant Convolutional Quantum Ansätze
We develop a theoretical framework for $S_n$-equivariant convolutional quantum circuits with SU$(d)$-symmetry, building on and significantly generalizing Jordan's Permutational Quantum Computing (PQC) formalism based on Schur-Weyl duality connecting both SU$(d)$ and $S_n$ actions on qudits.
Uncertainty Regularized Policy Learning for Offline Reinforcement Learning
URPL adds an uncertainty regularization term in the policy learning objective to enforce to learn a more stable policy under the offline setting.
Adaptive Q-learning for Interaction-Limited Reinforcement Learning
Specifically, we explicitly consider the difference between the online and offline data and apply an adaptive update scheme accordingly, i. e., a pessimistic update strategy for the offline dataset and a greedy or no pessimistic update scheme for the online dataset.
Towards a variational Jordan-Lee-Preskill quantum algorithm
Rapid developments of quantum information technology show promising opportunities for simulating quantum field theory in near-term quantum devices.
An Extendible, Graph-Neural-Network-Based Approach for Accurate Force Field Development of Large Flexible Organic Molecules
An accurate force field is the key to the success of all molecular mechanics simulations on organic polymers and biomolecules.
Cooperative Heterogeneous Deep Reinforcement Learning
Numerous deep reinforcement learning agents have been proposed, and each of them has its strengths and flaws.
An optimal hierarchical clustering approach to segmentation of mobile LiDAR point clouds
The main contribution of this paper is that we succeed to optimize the combination of clusters in the hierarchical clustering.
Road Curb Extraction from Mobile LiDAR Point Clouds
Automatic extraction of road curbs from uneven, unorganized, noisy and massive 3D point clouds is a challenging task.
