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Found 12 papers, 3 papers with code
Multi-scale Topology Optimization using Neural Networks
The neural network takes as input the local coordinates within a cell to represent the density distribution within a cell, as well as the global coordinates of each cell to design spatially varying microstructure cells.
Convergence Analysis of Discrete Diffusion Model: Exact Implementation through Uniformization
Diffusion models have achieved huge empirical success in data generation tasks.
BackdoorBench: A Comprehensive Benchmark and Analysis of Backdoor Learning
We hope that our efforts could build a solid foundation of backdoor learning to facilitate researchers to investigate existing algorithms, develop more innovative algorithms, and explore the intrinsic mechanism of backdoor learning.
Defenses in Adversarial Machine Learning: A Survey
Adversarial phenomenon has been widely observed in machine learning (ML) systems, especially in those using deep neural networks, describing that ML systems may produce inconsistent and incomprehensible predictions with humans at some particular cases.
The $L^\infty$ Learnability of Reproducing Kernel Hilbert Spaces
We prove that if $\beta$ is independent of the input dimension $d$, then functions in the RKHS can be learned efficiently under the $L^\infty$ norm, i. e., the sample complexity depends polynomially on $d$.
Versatile Backdoor Attack with Visible, Semantic, Sample-Specific, and Compatible Triggers
Deep neural networks (DNNs) can be manipulated to exhibit specific behaviors when exposed to specific trigger patterns, without affecting their performance on benign samples, dubbed \textit{backdoor attack}.
Topology Optimization using Neural Networks with Conditioning Field Initialization for Improved Efficiency
We employ the strain energy field calculated on the initial design domain as an additional conditioning field input to the neural network throughout the optimization.
A duality framework for generalization analysis of random feature models and two-layer neural networks
The first application is to study learning functions in $\mathcal{F}_{p,\pi}$ with RFMs.
DMF-TONN: Direct Mesh-free Topology Optimization using Neural Networks
We show that using a suitable Fourier Features neural network architecture and hyperparameters, the density field approximation neural network can learn the weights to represent the optimal density field for the given domain and boundary conditions, by directly backpropagating the loss gradient through the displacement field approximation neural network, and unlike prior work there is no requirement of a sensitivity filter, optimality criterion method, or a separate training of density network in each topology optimization iteration.
Improved Analysis of Score-based Generative Modeling: User-Friendly Bounds under Minimal Smoothness Assumptions
We give an improved theoretical analysis of score-based generative modeling.
Concurrent build direction, part segmentation, and topology optimization for additive manufacturing using neural networks
Through training, the network learns a material density and segment classification in the continuous 3D space.
BackdoorBench: A Comprehensive Benchmark of Backdoor Learning
However, we find that the evaluations of new methods are often unthorough to verify their claims and accurate performance, mainly due to the rapid development, diverse settings, and the difficulties of implementation and reproducibility.
