arXiv research

Bayesian optimization of antibodies learns from immune system evolution.

AntBO optimizes antibody design using Bayesian optimization for efficient and effective CDRH3 sequence generation.

BOAT optimizes multiple antibody properties efficiently.

Two ML frameworks predict antibody properties using structural data.

AbDiffuser generates full-atom antibodies with sequence and structure fidelity.

Antibodies are a critical part of the immune system, having the function of directly neutralising or tagging undesirable objects (the antigens) for future destruction. Being able to predict which amino acids belong to the paratope, the region on the antibody which binds to the antigen, can facilitate antibody design an…

Improved antibody humanness prediction using patent data.

Active learning speeds up antibody affinity prediction.

Method scales up ML science by measuring multiple molecules at once.

K-Models clusters functional data with ordinal constraints for better interpretability.

Estimates vaccine effectiveness and immune correlates in TND studies with missing data.

New method combines regional HIV prevention trial data without sharing individual patient info.

New algorithms speed up learning from large screens of proteins.

Improved GAN performance using higher-order Wasserstein moments.

Branching Flows generates sequences of varying lengths using binary trees.

Super learner uses diverse screeners to improve prediction performance.

Proposes a framework to assess feature importance without algorithm constraints.

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

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

Unified approach to experimental design using interlacing polynomials.

We propose a new low-cost machine-learning-based methodology which assists designers in reducing the gap between the problem and the solution in the design process. Our work applies reinforcement learning (RL) to find the optimal task-oriented design solution through the construction of the design action for each task.…

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.

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

Deep learning models enhance engineering design automation.

Efficient deep learning computing requires algorithm and hardware co-design to enable specialization: we usually need to change the algorithm to reduce memory footprint and improve energy efficiency. However, the extra degree of freedom from the algorithm makes the design space much larger: it's not only about designin…

DAD learns to design experiments quickly, outperforming traditional methods.

Enhances scenario approach for certifying design properties post-design.

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.

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

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…

Generative systems have a significant potential to synthesize innovative design alternatives. Still, most of the common systems that have been adopted in design require the designer to explicitly define the specifications of the procedures and in some cases the design space. In contrast, a generative system could poten…

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…

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,…