Search Results for author:
Found 34 papers, 8 papers with code
A solution for the mean parametrization of the von Mises-Fisher distribution
The von Mises-Fisher distribution as an exponential family can be expressed in terms of either its natural or its mean parameters.
Prediction under Latent Subgroup Shifts with High-Dimensional Observations
We introduce a new approach to prediction in graphical models with latent-shift adaptation, i. e., where source and target environments differ in the distribution of an unobserved confounding latent variable.
Successor-Predecessor Intrinsic Exploration
Here we focus on exploration with intrinsic rewards, where the agent transiently augments the external rewards with self-generated intrinsic rewards.
A Unified Theory of Dual-Process Control
We apply a single model based on this observation to findings from research on executive control, reward-based learning, and judgment and decision making, showing that seemingly diverse dual-process phenomena can be understood as domain-specific consequences of a single underlying set of computational principles.
Unsupervised representation learning with recognition-parametrised probabilistic models
We introduce a new approach to probabilistic unsupervised learning based on the recognition-parametrised model (RPM): a normalised semi-parametric hypothesis class for joint distributions over observed and latent variables.
Structured Recognition for Generative Models with Explaining Away
A key goal of unsupervised learning is to go beyond density estimation and sample generation to reveal the structure inherent within observed data.
Minimum Description Length Control
We propose a novel framework for multitask reinforcement learning based on the minimum description length (MDL) principle.
Divergent representations of ethological visual inputs emerge from supervised, unsupervised, and reinforcement learning
Artificial neural systems trained using reinforcement, supervised, and unsupervised learning all acquire internal representations of high dimensional input.
Probabilistic Tensor Decomposition of Neural Population Spiking Activity
The firing of neural populations is coordinated across cells, in time, and across experimentalconditions or repeated experimental trials; and so a full understanding of the computationalsignificance of neural responses must be based on a separation of these different contributions tostructured activity. Tensor decomposition is an approach to untangling the influence of multiple factors in data that iscommon in many fields.
A First-Occupancy Representation for Reinforcement Learning
Both animals and artificial agents benefit from state representations that support rapid transfer of learning across tasks and which enable them to efficiently traverse their environments to reach rewarding states.
Organizing recurrent network dynamics by task-computation to enable continual learning
Here, we develop a novel learning rule designed to minimize interference between sequentially learned tasks in recurrent networks.
Non-reversible Gaussian processes for identifying latent dynamical structure in neural data
Here, we propose a new family of “dynamical” priors over trajectories, in the form of GP covariance functions that express a property shared by most dynamical systems: temporal non-reversibility.
Amortised Learning by Wake-Sleep
Models that employ latent variables to capture structure in observed data lie at the heart of many current unsupervised learning algorithms, but exact maximum-likelihood learning for powerful and flexible latent-variable models is almost always intractable.
A neurally plausible model for online recognition and postdiction in a dynamical environment
Humans and other animals are frequently near-optimal in their ability to integrate noisy and ambiguous sensory data to form robust percepts---which are informed both by sensory evidence and by prior expectations about the structure of the environment.
A neurally plausible model learns successor representations in partially observable environments
Animals need to devise strategies to maximize returns while interacting with their environment based on incoming noisy sensory observations.
Kernel Instrumental Variable Regression
Instrumental variable (IV) regression is a strategy for learning causal relationships in observational data.
Learning interpretable continuous-time models of latent stochastic dynamical systems
We develop an approach to learn an interpretable semi-parametric model of a latent continuous-time stochastic dynamical system, assuming noisy high-dimensional outputs sampled at uneven times.
Temporal alignment and latent Gaussian process factor inference in population spike trains
We introduce a novel scalable approach to identifying common latent structure in neural population spike-trains, which allows for variability both in the trajectory and in the rate of progression of the underlying computation.
Empirical fixed point bifurcation analysis
In a common experimental setting, the behaviour of a noisy dynamical system is monitored in response to manipulations of one or more control parameters.
Flexible and accurate inference and learning for deep generative models
We introduce a new approach to learning in hierarchical latent-variable generative models called the "distributed distributional code Helmholtz machine", which emphasises flexibility and accuracy in the inferential process.
A Universal Marginalizer for Amortized Inference in Generative Models
We consider the problem of inference in a causal generative model where the set of available observations differs between data instances.
Bayesian Manifold Learning: The Locally Linear Latent Variable Model (LL-LVM)
We introduce the Locally Linear Latent Variable Model (LL-LVM), a probabilistic model for non-linear manifold discovery that describes a joint distribution over observations, their manifold coordinates and locally linear maps conditioned on a set of neighbourhood relationships.
Extracting regions of interest from biological images with convolutional sparse block coding
We perform extensive experiments on simulated images and the inference algorithm consistently recovers a large proportion of the cells with a small number of false positives.
Recurrent linear models of simultaneously-recorded neural populations
We show that RLMs describe motor-cortical population data better than either directly-coupled generalised-linear models or latent linear dynamical system models with generalised-linear observations.
Regularization and nonlinearities for neural language models: when are they needed?
We develop a slightly modified IRLM that separates long-context units (LCUs) from short-context units and show that the LCUs alone achieve a state-of-the-art performance on the MRSC task of 60. 8%.
Spectral learning of linear dynamics from generalised-linear observations with application to neural population data
Here, we show how spectral learning methods for linear systems with Gaussian observations (usually called subspace identification in this context) can be extended to estimate the parameters of dynamical system models observed through non-Gaussian noise models.
Learning visual motion in recurrent neural networks
We present a dynamic nonlinear generative model for visual motion based on a latent representation of binary-gated Gaussian variables.
Probabilistic amplitude and frequency demodulation
A number of recent scientific and engineering problems require signals to be decomposed into a product of a slowly varying positive envelope and a quickly varying carrier whose instantaneous frequency also varies slowly over time.
Dynamical segmentation of single trials from population neural data
Simultaneous recordings of many neurons embedded within a recurrently-connected cortical network may provide concurrent views into the dynamical processes of that network, and thus its computational function.
Empirical models of spiking in neural populations
Neurons in the neocortex code and compute as part of a locally interconnected population.
Occlusive Components Analysis
We show that the object parameters can be learnt from an unlabelled set of images in which objects occlude one another.
Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity
We applied these methods to the activity of 61 neurons recorded simultaneously in macaque premotor and motor cortices during reach planning and execution.
On Sparsity and Overcompleteness in Image Models
Computational models of visual cortex, and in particular those based on sparse coding, have enjoyed much recent attention.
Modeling Natural Sounds with Modulation Cascade Processes
Natural sounds are structured on many time-scales.
