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Found 11 papers, 2 papers with code
Towards Reverse-Engineering the Brain: Brain-Derived Neuromorphic Computing Approach with Photonic, Electronic, and Ionic Dynamicity in 3D integrated circuits
The human brain has immense learning capabilities at extreme energy efficiencies and scale that no artificial system has been able to match.
Perspectives for self-driving labs in synthetic biology
Self-driving labs (SDLs) combine fully automated experiments with artificial intelligence (AI) that decides the next set of experiments.
Compressed Predictive Information Coding
The key insight of our framework is to learn representations by minimizing the compression complexity and maximizing the predictive information in latent space.
Learning from learning machines: a new generation of AI technology to meet the needs of science
We outline emerging opportunities and challenges to enhance the utility of AI for scientific discovery.
Bayesian Inference in High-Dimensional Time-Serieswith the Orthogonal Stochastic Linear Mixing Model
Stochastic linear mixing models (SLMM) assume the mixture coefficients depend on input, making them more flexible and effective to capture complex output dependence.
Stochastic Collapsed Variational Inference for Structured Gaussian Process Regression Network
This paper presents an efficient variational inference framework for deriving a family of structured gaussian process regression network (SGPRN) models.
Sparse and Low-bias Estimation of High Dimensional Vector Autoregressive Models
A well-known feature of RML inference is that in general the technique produces a trade-off between sparsity and bias that depends on the choice of the regularization hyperparameter.
Unsupervised Discovery of Temporal Structure in Noisy Data with Dynamical Components Analysis
Linear dimensionality reduction methods are commonly used to extract low-dimensional structure from high-dimensional data.
Optimizing the Union of Intersections LASSO ($UoI_{LASSO}$) and Vector Autoregressive ($UoI_{VAR}$) Algorithms for Improved Statistical Estimation at Scale
The analysis of scientific data of increasing size and complexity requires statistical machine learning methods that are both interpretable and predictive.
Provably convergent acceleration in factored gradient descent with applications in matrix sensing
We present theoretical results on the convergence of \emph{non-convex} accelerated gradient descent in matrix factorization models with $\ell_2$-norm loss.
Modeling neural activity at the ensemble level
Here we demonstrate that the activity of neural ensembles can be quantitatively modeled.
Neurons and Cognition
