Insect-inspired neural nets boost machine learning accuracy with limited data.
problem Difficulty of machine learning with scarce training data.
method Deployed MothNet, a computational model of insect olfactory network, as a feature generator.
result Cyborgs improved test set accuracy by 6% to 33% compared to baseline ML methods.
In this study, we present and analyze a framework for geometric and topological estimation for mapping of unknown environments. We consider agents mimicking motion behaviors of cyborg insects, known as biobots, and exploit coordinate-free local interactions among them to infer geometric and topological information abou…
Crowdsourcing insect observations helps assess demographic shifts.
problem Difficulty in collecting sufficient scale insect census data.
method Gather observations from bystanders and enthusiasts.
result Interest exists for collaboration between enthusiasts and researchers.
Optimizes neuromorphic learning rules for specific tasks.
problem Understanding and fine-tuning learning rules for specific tasks.
method Integrates learning process into network, uses DeepHyper for optimization.
result Optimal learning rules can be dataset-dependent.
This study shows how social insects and machine learning methods share a common mathematical framework.
problem Understanding how decentralized systems achieve optimal decision-making.
method Developed a rigorous mathematical framework to show isomorphism between ant colonies and ensemble machine learning.
result Demonstrated that ant colony decision-making and random forest learning implement identical variance reduction strategies through decorrelation of identical units.
A novel algorithm optimizes sparsity in reservoir computing inspired by insect brain.
problem Optimizing sparsity in reservoir computing networks.
method Inspired by insect brain, the algorithm optimizes sparsity levels by adjusting node firing thresholds.
result The algorithm outperforms standard gradient descent on tasks involving better classification, memorization, and convergence.
Automated system identifies and counts insects from images.
problem Manual sorting and identification of insect samples is time-consuming and limits biodiversity mapping.
method Robot-enabled image-based identification machine using CNNs.
result Classification accuracy of 0.980 for initial dataset. A decentralized deep RL controller improves hexapod locomotion learning.
problem Deep RL struggles with real-world legged robot control.
method Decentralized deep RL on a hexapod robot.
result Decentralized approach learns better and faster.
VOPy optimizes multiple objectives with flexible cone-based ordering.
problem Optimizing multiple objectives with partial order constraints.
method Flexible cone-based ordering, modular architecture, integration of existing and novel methods.
result Advances black-box vector optimization in noisy, discrete, or limited budget settings.
Probit regression was first proposed by Bliss in 1934 to study mortality rates of insects. Since then, an extensive body of work has analyzed and used probit or related binary regression methods (such as logistic regression) in numerous applications and fields. This paper provides a fresh angle to such well-established…
Deep CNNs struggle with rare taxa; one-class classifiers help identify them.
problem Efficiently identifying rare benthic macroinvertebrates in insect monitoring.
method Combining deep CNNs with one-class classifiers to identify rare taxa.
result The proposed approach can improve rare species identification, supporting automation.
A new hashing method improves accuracy by learning an orthogonal transform.
problem Improving hashing accuracy for similarity search.
method Procrustean Orthogonal Sparse Hashing (POSH) method.
result POSH outperforms state-of-the-art hashing methods.
Graph-Hist classifies social media graphs using feature histograms.
problem Classifying large, sparse social media graphs.
method Extracts latent features, bins nodes, and classifies based on multi-channel histograms.
result Improves bot detection in social media graphs.
This paper generates natural-looking perturbations to fool classifiers.
problem Generating adversarial examples that mimic natural objects or signals.
method Employing generative adversarial networks and optimization algorithms.
result The approach can fool classification models in both image and audio domains.
(ABRIDGED) In previous work, two platforms have been developed for testing computer-vision algorithms for robotic planetary exploration (McGuire et al. 2004b,2005; Bartolo et al. 2007). The wearable-computer platform has been tested at geological and astrobiological field sites in Spain (Rivas Vaciamadrid and Riba de S…
ChemCrow enhances LLMs for chemistry tasks, automating complex chemical processes.
problem Limited access to computational chemistry tools for large-language models.
method Integrating 18 expert-designed chemistry tools into an LLM (ChemCrow).
result ChemCrow autonomously plans and executes chemical syntheses and discoveries.
Mosquitoes are a major vector for malaria, causing hundreds of thousands of deaths in the developing world each year. Not only is the prevention of mosquito bites of paramount importance to the reduction of malaria transmission cases, but understanding in more forensic detail the interplay between malaria, mosquito vec…
Paper addresses challenges in benchmarking stream learning algorithms with real-world data.
problem Lack of publicly available non-stationary real-world datasets for evaluating stream algorithms.
method Proposes a new public data repository for benchmarking stream algorithms with real-world data.
result Mitigates problems related to dataset choice in experimental evaluation of stream classifiers and drift detectors.