256 research outputs found
G-Mix: A Generalized Mixup Learning Framework Towards Flat Minima
Deep neural networks (DNNs) have demonstrated promising results in various
complex tasks. However, current DNNs encounter challenges with
over-parameterization, especially when there is limited training data
available. To enhance the generalization capability of DNNs, the Mixup
technique has gained popularity. Nevertheless, it still produces suboptimal
outcomes. Inspired by the successful Sharpness-Aware Minimization (SAM)
approach, which establishes a connection between the sharpness of the training
loss landscape and model generalization, we propose a new learning framework
called Generalized-Mixup, which combines the strengths of Mixup and SAM for
training DNN models. The theoretical analysis provided demonstrates how the
developed G-Mix framework enhances generalization. Additionally, to further
optimize DNN performance with the G-Mix framework, we introduce two novel
algorithms: Binary G-Mix and Decomposed G-Mix. These algorithms partition the
training data into two subsets based on the sharpness-sensitivity of each
example to address the issue of "manifold intrusion" in Mixup. Both theoretical
explanations and experimental results reveal that the proposed BG-Mix and
DG-Mix algorithms further enhance model generalization across multiple datasets
and models, achieving state-of-the-art performance.Comment: 19 pages, 23 figure
AdaER: An Adaptive Experience Replay Approach for Continual Lifelong Learning
Continual lifelong learning is an machine learning framework inspired by
human learning, where learners are trained to continuously acquire new
knowledge in a sequential manner. However, the non-stationary nature of
streaming training data poses a significant challenge known as catastrophic
forgetting, which refers to the rapid forgetting of previously learned
knowledge when new tasks are introduced. While some approaches, such as
experience replay (ER), have been proposed to mitigate this issue, their
performance remains limited, particularly in the class-incremental scenario
which is considered natural and highly challenging. In this paper, we present a
novel algorithm, called adaptive-experience replay (AdaER), to address the
challenge of continual lifelong learning. AdaER consists of two stages: memory
replay and memory update. In the memory replay stage, AdaER introduces a
contextually-cued memory recall (C-CMR) strategy, which selectively replays
memories that are most conflicting with the current input data in terms of both
data and task. Additionally, AdaER incorporates an entropy-balanced reservoir
sampling (E-BRS) strategy to enhance the performance of the memory buffer by
maximizing information entropy. To evaluate the effectiveness of AdaER, we
conduct experiments on established supervised continual lifelong learning
benchmarks, specifically focusing on class-incremental learning scenarios. The
results demonstrate that AdaER outperforms existing continual lifelong learning
baselines, highlighting its efficacy in mitigating catastrophic forgetting and
improving learning performance.Comment: 18 pages, 26 figure
A Comparative Analysis of Deep Reinforcement Learning-enabled Freeway Decision-making for Automated Vehicles
Deep reinforcement learning (DRL) is becoming a prevalent and powerful
methodology to address the artificial intelligent problems. Owing to its
tremendous potentials in self-learning and self-improvement, DRL is broadly
serviced in many research fields. This article conducted a comprehensive
comparison of multiple DRL approaches on the freeway decision-making problem
for autonomous vehicles. These techniques include the common deep Q learning
(DQL), double DQL (DDQL), dueling DQL, and prioritized replay DQL. First, the
reinforcement learning (RL) framework is introduced. As an extension, the
implementations of the above mentioned DRL methods are established
mathematically. Then, the freeway driving scenario for the automated vehicles
is constructed, wherein the decision-making problem is transferred as a control
optimization problem. Finally, a series of simulation experiments are achieved
to evaluate the control performance of these DRL-enabled decision-making
strategies. A comparative analysis is realized to connect the autonomous
driving results with the learning characteristics of these DRL techniques.Comment: 11 pages, 10 figure
Prioritized Planning for Target-Oriented Manipulation via Hierarchical Stacking Relationship Prediction
In scenarios involving the grasping of multiple targets, the learning of
stacking relationships between objects is fundamental for robots to execute
safely and efficiently. However, current methods lack subdivision for the
hierarchy of stacking relationship types. In scenes where objects are mostly
stacked in an orderly manner, they are incapable of performing human-like and
high-efficient grasping decisions. This paper proposes a perception-planning
method to distinguish different stacking types between objects and generate
prioritized manipulation order decisions based on given target designations. We
utilize a Hierarchical Stacking Relationship Network (HSRN) to discriminate the
hierarchy of stacking and generate a refined Stacking Relationship Tree (SRT)
for relationship description. Considering that objects with high stacking
stability can be grasped together if necessary, we introduce an elaborate
decision-making planner based on the Partially Observable Markov Decision
Process (POMDP), which leverages observations and generates the least
grasp-consuming decision chain with robustness and is suitable for
simultaneously specifying multiple targets. To verify our work, we set the
scene to the dining table and augment the REGRAD dataset with a set of common
tableware models for network training. Experiments show that our method
effectively generates grasping decisions that conform to human requirements,
and improves the implementation efficiency compared with existing methods on
the basis of guaranteeing the success rate.Comment: 8 pages, 8 figure
Quartz sand surface morphology of granitic tafoni at Laoshan, China
43-48In this study, a SEM method was used to analyze the surface morphology of the quartz sand granitic tafoni at Laoshan, for the purpose of exploring the weathering process of this tafoni. Present study showed that granitic tafoni at Laoshan, the quartz sand roundness was dominated by angular and sub-angular morphologies. Massive Hydrodynamic features had been developed on the quartz sand surfaces, as well as wind and chemistry forms, which were more developed. It was determined that granitic tafoni at Laoshan, the quartz sand had suffered long-term rainy and windy mechanical erosion, as well as chemical dissolution from residual pit water. These findings differed from the earlier views that the tafone was formed by the glacial melt water
Prospect of detecting magnetic fields from strong-magnetized binary neutron stars
Binary neutron star mergers are unique sources of gravitational waves in
multi-messenger astronomy. The inspiral phase of binary neutron stars can emit
gravitational waves as chirp signals. The present waveform models of
gravitational wave only considered the gravitational interaction. In this
paper, we derive the waveform of the gravitational wave signal taking into
account the presence of magnetic fields. We found that the electromagnetic
interaction and radiation can introduce different frequency-dependent power
laws for both amplitude and frequency of the gravitational wave. We show from
the results of Fisher information matrix that the third-generation observation
may detect magnetic dipole moments if the magnetic field is around 10^17 G
- …