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Found 12 papers, 4 papers with code
Explainable Fuzzy Neural Network with Multi-Fidelity Reinforcement Learning for Micro-Architecture Design Space Exploration
With the continuous advancement of processors, modern micro-architecture designs have become increasingly complex.
Two-way Node Popularity Model for Directed and Bipartite Networks
There has been extensive research on community detection in directed and bipartite networks.
Polynomial Composition Activations: Unleashing the Dynamics of Large Language Models
Transformers have found extensive applications across various domains due to the powerful fitting capabilities.
Breathing cluster in complex neuron-astrocyte networks
It is revealed that the contents of the cluster are determined by the network symmetry and the breathing activities are due to the interplay between the neural network and the astrocyte.
Reconstruct from BEV: A 3D Lane Detection Approach based on Geometry Structure Prior
Inspired by previous methods, we first analyze the geometry heuristic between the 3D lane and its 2D representation on the ground and propose to impose explicit supervision based on the structure prior, which makes it achievable to build inter-lane and intra-lane relationships to facilitate the reconstruction of 3D lanes from local to global.
Reconstruct from Top View: A 3D Lane Detection Approach based on Geometry Structure Prior
Inspired by previous methods, we first analyze the geometry heuristic between the 3D lane and its 2D representation on the ground and propose to impose explicit supervision based on the structure prior, which makes it achievable to build inter-lane and intra-lane relationships to facilitate the reconstruction of 3D lanes from local to global.
Ranked #7 on
3D Lane Detection
on Apollo Synthetic 3D Lane
Knowledge Augmented Machine Learning with Applications in Autonomous Driving: A Survey
The availability of representative datasets is an essential prerequisite for many successful artificial intelligence and machine learning models.
Fine-Grained AutoAugmentation for Multi-Label Classification
Data augmentation is a commonly used approach to improving the generalization of deep learning models.
Beating the Standard Quantum Limit under Ambient Conditions with Solid-State Spins
However, it has not been realized in solid-state spin systems at ambient conditions, owing to its intrinsic complexity for the preparation and survival of pure and entangled quantum states.
Quantum Physics
A Logical Model for Supporting Social Commonsense Knowledge Acquisition
To make machine exhibit human-like abilities in the domains like robotics and conversation, social commonsense knowledge (SCK), i. e., common sense about social contexts and social roles, is absolutely necessarily.
Multi-Label Classification with Label Graph Superimposing
In this paper, we propose a label graph superimposing framework to improve the conventional GCN+CNN framework developed for multi-label recognition in the following two aspects.
Ranked #30 on
Multi-Label Classification
on MS-COCO
Room-temperature implementation of the Deutsch-Jozsa algorithm with a single electronic spin in diamond
The nitrogen-vacancy defect center (NV center) is a promising candidate for quantum information processing due to the possibility of coherent manipulation of individual spins in the absence of the cryogenic requirement.
Quantum Physics
