arXiv research

Paper proposes a machine learning framework for VLSI mask optimization.

A new SNN learning algorithm for energy-efficient VLSI circuits.

Paper proposes active learning for hotspot detection in VLSI design.

New method for clustering hypergraphs using modularity maximization.

SQWA improves low-precision DNNs with model averaging and quantization.

Recent research has shown that performance in signal processing tasks can often be significantly improved by using signal models based on sparse representations, where a signal is approximated using a small number of elements from a fixed dictionary. Unfortunately, inference in this model involves solving non-smooth op…

Deep learning predicts etch patterns with high accuracy from limited data.

A framework for faster, better infographic design by non-experts and experts alike.

A new RL method helps designers solve complex tasks.

The paper proposes a method to reliably select design algorithms for machine learning-guided design tasks.

Unified approach to experimental design using interlacing polynomials.

Design-by-Morphing creates radical airfoil designs without geometric constraints.

MO-PaDGAN generates diverse, high-performance designs with multiple metrics.

MCD automates counterfactual design searches for multi-modal tasks.

PaDGAN generates diverse, high-quality designs with improved performance.

New approach designs optimal structures using GANs and CNNs.

ANN with GA optimizes flexible disc design for lower mass and stress.

DeepCloud uses machine learning to generate design alternatives without explicit designer input.

New method for mixed-variable GSA improves material design efficiency.

Deep learning models enhance engineering design automation.

Design automation optimizes deep learning models for various hardware.

Enhances scenario approach for certifying design properties post-design.

DAD learns to design experiments quickly, outperforming traditional methods.

MO-PaDGAN improves multi-objective optimization by generating diverse and high-performing designs.

Generative thermal design learns optimal shapes using multi-agent reinforcement learning.

Develops a prediction method based on sampling design.

Solving the RNA inverse folding problem is a critical prerequisite to RNA design, an emerging field in bioengineering with a broad range of applications from reaction catalysis to cancer therapy. Although significant progress has been made in developing machine-based inverse RNA folding algorithms, current approaches s…

Paper proposes NASAIC framework for co-designing neural architectures and heterogeneous ASICs.

Adaptive designs achieve strong Neyman regret guarantees for ATE estimation.

iDAD uses neural networks to quickly adapt experiments without likelihoods.

Unified framework for combinatorial and rounding algorithms in experimental design.

New method refines model predictions as design evolves.

A new method achieves optimal uniformity in designs with minimal flexibility.

Optimizes PCB stack-up design for high-speed circuits.

Machine learning improves polymer design accuracy.

RID-Noise improves robust design under noisy conditions using neural networks.

Proposes a new adaptive design strategy for discontinuous regression functions.

The design of multiple experiments is commonly undertaken via suboptimal strategies, such as batch (open-loop) design that omits feedback or greedy (myopic) design that does not account for future effects. This paper introduces new strategies for the optimal design of sequential experiments. First, we rigorously formul…

Dataset of 4500 bicycle designs aids in design analysis and synthesis.

This paper proposes a new approach to construct high quality space-filling sample designs. First, we propose a novel technique to quantify the space-filling property and optimally trade-off uniformity and randomness in sample designs in arbitrary dimensions. Second, we connect the proposed metric (defined in the spatia…

Machine learning predicts and generates appealing car designs.

Layout design with complex constraints is a challenging problem to solve due to the non-uniqueness of the solution and the difficulties in incorporating the constraints into the conventional optimization-based methods. In this paper, we propose a design method based on the recently developed machine learning technique,…

New deep learning method simplifies parameter estimation design.

Neural network ensembles predict design rule violations from early stages of IC design.

Calibrating materials to engineering targets improves wind turbine design.

We employ a 2x3 factorial experiment to study two central factors in the design of prediction markets (PMs) for idea evaluation: the overall design of the PM, and the elasticity of market prices set by a market maker. The results show that 'multi-market designs' on which each contract is traded on a separate PM lead to…

vsOED optimizes experiment design with reinforcement learning for Bayesian models.

PSO improves $G$-optimal designs for up to 5 factors, reducing computation time.