146 research outputs found
Mitigating the Accuracy-Robustness Trade-off via Multi-Teacher Adversarial Distillation
Adversarial training is a practical approach for improving the robustness of
deep neural networks against adversarial attacks. Although bringing reliable
robustness, the performance toward clean examples is negatively affected after
adversarial training, which means a trade-off exists between accuracy and
robustness. Recently, some studies have tried to use knowledge distillation
methods in adversarial training, achieving competitive performance in improving
the robustness but the accuracy for clean samples is still limited. In this
paper, to mitigate the accuracy-robustness trade-off, we introduce the
Multi-Teacher Adversarial Robustness Distillation (MTARD) to guide the model's
adversarial training process by applying a strong clean teacher and a strong
robust teacher to handle the clean examples and adversarial examples,
respectively. During the optimization process, to ensure that different
teachers show similar knowledge scales, we design the Entropy-Based Balance
algorithm to adjust the teacher's temperature and keep the teachers'
information entropy consistent. Besides, to ensure that the student has a
relatively consistent learning speed from multiple teachers, we propose the
Normalization Loss Balance algorithm to adjust the learning weights of
different types of knowledge. A series of experiments conducted on public
datasets demonstrate that MTARD outperforms the state-of-the-art adversarial
training and distillation methods against various adversarial attacks
Learning Specialized Activation Functions for Physics-informed Neural Networks
Physics-informed neural networks (PINNs) are known to suffer from
optimization difficulty. In this work, we reveal the connection between the
optimization difficulty of PINNs and activation functions. Specifically, we
show that PINNs exhibit high sensitivity to activation functions when solving
PDEs with distinct properties. Existing works usually choose activation
functions by inefficient trial-and-error. To avoid the inefficient manual
selection and to alleviate the optimization difficulty of PINNs, we introduce
adaptive activation functions to search for the optimal function when solving
different problems. We compare different adaptive activation functions and
discuss their limitations in the context of PINNs. Furthermore, we propose to
tailor the idea of learning combinations of candidate activation functions to
the PINNs optimization, which has a higher requirement for the smoothness and
diversity on learned functions. This is achieved by removing activation
functions which cannot provide higher-order derivatives from the candidate set
and incorporating elementary functions with different properties according to
our prior knowledge about the PDE at hand. We further enhance the search space
with adaptive slopes. The proposed adaptive activation function can be used to
solve different PDE systems in an interpretable way. Its effectiveness is
demonstrated on a series of benchmarks. Code is available at
https://github.com/LeapLabTHU/AdaAFforPINNs
Fix-and-Optimize and Variable Neighborhood Search Approaches for Stochastic Multi-Item Capacitated Lot-Sizing Problems
We discuss stochastic multi-item capacitated lot-sizing problems with and without setup carryovers (also known as link lot size), S-MICLSP and S-MICLSP-L. The two models are motivated from a real-world steel enterprise. To overcome the nonlinearity of the models, a piecewise linear approximation method is proposed. We develop a new fix-and-optimize (FO) approach to solve the approximated models. Compared with the existing FO approach(es), our FO is based on the concept of “k-degree-connection” for decomposing the problems. Furthermore, we also propose an integrative approach combining our FO and variable neighborhood search (FO-VNS), which can improve the solution quality of our FO approach by diversifying the search space. Numerical experiments are performed on the instances following the nature of realistic steel products. Our approximation method is shown to be efficient. The results also show that the proposed FO and FO-VNS approaches significantly outperform the recent FO approaches, and the FO-VNS approaches can be more outstanding on the solution quality with moderate computational effort
Hundreds Guide Millions: Adaptive Offline Reinforcement Learning with Expert Guidance
Offline reinforcement learning (RL) optimizes the policy on a previously
collected dataset without any interactions with the environment, yet usually
suffers from the distributional shift problem. To mitigate this issue, a
typical solution is to impose a policy constraint on a policy improvement
objective. However, existing methods generally adopt a ``one-size-fits-all''
practice, i.e., keeping only a single improvement-constraint balance for all
the samples in a mini-batch or even the entire offline dataset. In this work,
we argue that different samples should be treated with different policy
constraint intensities. Based on this idea, a novel plug-in approach named
Guided Offline RL (GORL) is proposed. GORL employs a guiding network, along
with only a few expert demonstrations, to adaptively determine the relative
importance of the policy improvement and policy constraint for every sample. We
theoretically prove that the guidance provided by our method is rational and
near-optimal. Extensive experiments on various environments suggest that GORL
can be easily installed on most offline RL algorithms with statistically
significant performance improvements
Collaborative Edge Caching: a Meta Reinforcement Learning Approach with Edge Sampling
Current learning-based edge caching schemes usually suffer from dynamic
content popularity, e.g., in the emerging short video platforms, users' request
patterns shift significantly over time and across different edges. An intuitive
solution for a specific local edge cache is to collect more request histories
from other edge caches. However, uniformly merging these request histories may
not perform satisfactorily due to heterogeneous content distributions on
different edges. To solve this problem, we propose a collaborative edge caching
framework. First, we design a meta-learning-based collaborative strategy to
guarantee that the local model can timely meet the continually changing content
popularity. Then, we design an edge sampling method to select more "valuable"
neighbor edges to participate in the local training. To evaluate the proposed
framework, we conduct trace-driven experiments to demonstrate the effectiveness
of our design: it improves the average cache hit rate by up to
(normalized) compared with other baselines.Comment: Published on IEEE International Conference on Multimedia and Expo
2023 (ICME2023
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