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Results for kernel code

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11 papers · ranked by Valyu relevance

01
BbioRxiv31 Mar 2021
Population Codes Enable Learning from Few Examples By Shaping Inductive Bias
Blake Bordelon, Cengiz Pehlevan
The brain can learn from a limited number of experiences, an ability which requires suitable built in assumptions about the nature of the tasks which must be learned, or inductive biases. While inductive biases are central components of intelligence, how they are reflected in and shaped by population codes are not…
70relevance
02
BbioRxiv28 Apr 2020
Larger GPU-accelerated brain simulations with procedural connectivity
James C Knight, Thomas Nowotny
Large-scale simulations of spiking neural network models are an important tool for improving our understanding of the dynamics and ultimately the function of brains. However, even small mammals such as mice have on the order of 1 × 10^12^ synaptic connections which, in simulations, are each typically charaterized by at…
69relevance
03
BbioRxiv23 Apr 2020
FastSK: Fast Sequence Analysis with Gapped String Kernels
Derrick Blakely, Eamon Collins, Ritambhara Singh, Yanjun Qi
Gapped k-mer kernels with Support Vector Machines (gkm-SVMs) have achieved strong predictive performance on regulatory DNA sequences on modestly-sized training sets. However, existing gkm-SVM algorithms suffer from slow kernel computation time, as they depend exponentially on the sub-sequence feature-length, number of…
64relevance
04
BbioRxiv10 May 2021
Compositional Data Analysis using Kernels in Mass Cytometry Data
Pratyaydipta Rudra, Ryan Baxter, Elena WY Hsieh, Debashis Ghosh
Cell type abundance data arising from mass cytometry experiments are compositional in nature. Classical association tests do not apply to the compositional data due to their non-Euclidean nature. Existing methods for analysis of cell type abundance data suffer from several limitations for high-dimensional mass…
63relevance
05
CchemRxiv10 Mar 2022
Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels
Ping Yang, E. Adrian Henle, Xiaoli Fern, Cory M. Simon
Pesticides benefit agriculture by increasing crop yield, quality, and security. However, pesticides may inadvertently harm bees, which are agriculturally and ecologically vital as pollinators. The development of new pesticides---driven by pest resistance to and demands to reduce negative environmental impacts of…
61relevance
06
CchemRxiv30 Nov 2022
QM/MM Simulations on NVIDIA and AMD GPUs
Madushanka Manathunga, Hasan Metin Aktulga, Andreas W. Goetz, Kenneth M. Merz + 1 more
We have ported and optimized the GPU accelerated QUICK and AMBER based ab initio QM/MM implementation on AMD GPUs. This encompasses the entire Fock matrix build and force calculation in QUICK including one-electron integrals, two-electron repulsion integrals, exchange-correlation quadrature, and linear algebra…
58relevance
07
CchemRxiv5 Apr 2022
Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels
Ping Yang, E. Adrian Henle, Xiaoli Fern, Cory M. Simon
Pesticides benefit agriculture by increasing crop yield, quality, and security. However, pesticides may inadvertently harm bees, which are agriculturally and ecologically vital as pollinators. The development of new pesticides---driven by pest resistance to and demands to reduce negative environmental impacts of…
58relevance
08
BbioRxiv3 Aug 2020
GridFree: A Python Package of Image Analysis for Interactive Grain Counting and Measuring
Yang Hu, Zhiwu Zhang
Grain characteristics, including kernel length, kernel width, and thousand kernel weight, are critical component traits for grain yield. Manual measurements and counting are expensive, forming the bottleneck for dissecting the genetic architecture of these traits toward ultimate yield improvement. High-throughput…
56relevance
09
BbioRxiv20 Jul 2026
Factorization and spatial encodings: a hypothesis about the foundations of the genomic code
Karim G. Habashy, Benjamin D. Evans, Dan F. M. Goodman, Jeffrey S. Bowers
The genomic mechanisms that efficiently encode the initial architecture and synaptic connectivity of neural circuits remain poorly understood. We hypothesise that two primary mechanisms — spatial encoding and factorisation — enable a limited genome to initialise networks of billions of neurons. Spatial encoding, a form…
54relevance
10
CchemRxiv15 Jun 2022
Krein Support Vector Machine Classification of Antimicrobial Peptides
Joseph Redshaw, Darren Ting, Alex Brown, Jonathan Hirst + 1 more
Antimicrobial peptides (AMPs) represent a potential solution to the growing problem of antimicrobial resistance, yet their identification through wet-lab experiments is a costly and timeconsuming process. Accurate computational predictions would allow rapid in silico screening of candidate AMPs, thereby accelerating…
53relevance
11
CchemRxiv21 Dec 2022
kMCpy: A Python Package to Simulate Transport Properties in Solids with Kinetic Monte Carlo
Zeyu Deng, Tara P. Mishra, Weihang Xie, Daanyal Ahmed Saeed + 2 more
Understanding ion transport in functional materials is crucial to unravel complex chemical reactions, improve rate per- formance of materials for energy storage and conversion, and optimize catalysts. To model ionic transport, atomistic simulations, including molecular dynamics (MD) and kinetic Monte Carlo (kMC) have…
52relevance

Results for kernel code

Population Codes Enable Learning from Few Examples By Shaping Inductive Bias

Larger GPU-accelerated brain simulations with procedural connectivity

FastSK: Fast Sequence Analysis with Gapped String Kernels

Compositional Data Analysis using Kernels in Mass Cytometry Data

Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels

QM/MM Simulations on NVIDIA and AMD GPUs

Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels

GridFree: A Python Package of Image Analysis for Interactive Grain Counting and Measuring

Factorization and spatial encodings: a hypothesis about the foundations of the genomic code

Krein Support Vector Machine Classification of Antimicrobial Peptides

kMCpy: A Python Package to Simulate Transport Properties in Solids with Kinetic Monte Carlo

Population Codes Enable Learning from Few Examples By Shaping Inductive Bias

Larger GPU-accelerated brain simulations with procedural connectivity

FastSK: Fast Sequence Analysis with Gapped String Kernels

Compositional Data Analysis using Kernels in Mass Cytometry Data

Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels

QM/MM Simulations on NVIDIA and AMD GPUs

Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels

GridFree: A Python Package of Image Analysis for Interactive Grain Counting and Measuring

Factorization and spatial encodings: a hypothesis about the foundations of the genomic code

Krein Support Vector Machine Classification of Antimicrobial Peptides

kMCpy: A Python Package to Simulate Transport Properties in Solids with Kinetic Monte Carlo