arXiv research

A similarity metric for icon sets simplifies design selection.

Detecting PE malware files is now commonly approached using statistical and machine learning models. While these models commonly use features extracted from the structure of PE files, we propose that icons from these files can also help better predict malware. We propose an innovative machine learning approach to extra…

ICON learns differential equation operators from examples, revealing probabilistic inference.

ICON-OCnet solves optimal execution problems with neural networks and few examples.

PerceptNet learns haptic signal similarity using human data.

Survey explores geometric aspects of policy optimization in control systems.

ICON learns differential equation operators from prompts, reducing retraining and improving few-shot learning.

In this paper we establish rigorous benchmarks for image classifier robustness. Our first benchmark, ImageNet-C, standardizes and expands the corruption robustness topic, while showing which classifiers are preferable in safety-critical applications. Unlike recent robustness research, this benchmark evaluates performan…

Boosting improves accuracy by combining weak learners into a voting classifier.

BOSS learns from one labeled sample per class to match fully supervised performance.

Generative Adversarial Networks (GAN) boast impressive capacity to generate realistic images. However, like much of the field of deep learning, they require an inordinate amount of data to produce results, thereby limiting their usefulness in generating novelty. In the same vein, recent advances in meta-learning have o…

This paper is concerned with "nice" compactifications of manifolds. Siebenmann's iconic dissertation characterized open manifolds M^m (m>5) compactifiable by addition of a manifold boundary. His theorem extends easily to cases where M^m is noncompact with compact boundary; however, when Bd(M^m) is noncompact, the situa…

Unified facial behavior analysis network improves performance across tasks.

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.

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.

Topology design optimization offers tremendous opportunity in design and manufacturing freedoms by designing and producing a part from the ground-up without a meaningful initial design as required by conventional shape design optimization approaches. Ideally, with adequate problem statements, to formulate and solve the…

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.

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.