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Found 28 papers, 10 papers with code
Neural Processes
A neural network (NN) is a parameterised function that can be tuned via gradient descent to approximate a labelled collection of data with high precision.
Conditional Neural Processes
Deep neural networks excel at function approximation, yet they are typically trained from scratch for each new function.
Semi-Supervised Learning with Deep Generative Models
The ever-increasing size of modern data sets combined with the difficulty of obtaining label information has made semi-supervised learning one of the problems of significant practical importance in modern data analysis.
Ranked #53 on
Image Classification
on SVHN
NeRF-VAE: A Geometry Aware 3D Scene Generative Model
We propose NeRF-VAE, a 3D scene generative model that incorporates geometric structure via NeRF and differentiable volume rendering.
Continual Repeated Annealed Flow Transport Monte Carlo
We propose Continual Repeated Annealed Flow Transport Monte Carlo (CRAFT), a method that combines a sequential Monte Carlo (SMC) sampler (itself a generalization of Annealed Importance Sampling) with variational inference using normalizing flows.
Towards Interpretable Reinforcement Learning Using Attention Augmented Agents
Inspired by recent work in attention models for image captioning and question answering, we present a soft attention model for the reinforcement learning domain.
Beyond Greedy Ranking: Slate Optimization via List-CVAE
The conventional solution to the recommendation problem greedily ranks individual document candidates by prediction scores.
Variational Memory Addressing in Generative Models
Aiming to augment generative models with external memory, we interpret the output of a memory module with stochastic addressing as a conditional mixture distribution, where a read operation corresponds to sampling a discrete memory address and retrieving the corresponding content from memory.
Variational inference for Monte Carlo objectives
Recent progress in deep latent variable models has largely been driven by the development of flexible and scalable variational inference methods.
Conditional Set Generation with Transformers
An example of such a generator is the DeepSet Prediction Network (DSPN).
Generative Temporal Models with Memory
We consider the general problem of modeling temporal data with long-range dependencies, wherein new observations are fully or partially predictable based on temporally-distant, past observations.
Consistent Generative Query Networks
These models typically generate future frames in an autoregressive fashion, which is slow and requires the input and output frames to be consecutive.
Learning models for visual 3D localization with implicit mapping
We consider learning based methods for visual localization that do not require the construction of explicit maps in the form of point clouds or voxels.
Variational Learning for Recurrent Spiking Networks
We derive a plausible learning rule updating the synaptic efficacies for feedforward, feedback and lateral connections between observed and latent neurons.
S3TA: A Soft, Spatial, Sequential, Top-Down Attention Model
We present a soft, spatial, sequential, top-down attention model (S3TA).
Causally Correct Partial Models for Reinforcement Learning
In reinforcement learning, we can learn a model of future observations and rewards, and use it to plan the agent's next actions.
Amortized learning of neural causal representations
The CRN represent causal models using continuous representations and hence could scale much better with the number of variables.
Flow-based sampling for fermionic lattice field theories
Algorithms based on normalizing flows are emerging as promising machine learning approaches to sampling complicated probability distributions in a way that can be made asymptotically exact.
Implicit Riemannian Concave Potential Maps
We are interested in the challenging problem of modelling densities on Riemannian manifolds with a known symmetry group using normalising flows.
PARTS: Unsupervised Segmentation With Slots, Attention and Independence Maximization
We present a model that is able to segment visual scenes from complex 3D environments into distinct objects, learn disentangled representations of individual objects, and form consistent and coherent predictions of future frames, in a fully unsupervised manner.
Flow-based sampling in the lattice Schwinger model at criticality
In this work, we provide a numerical demonstration of robust flow-based sampling in the Schwinger model at the critical value of the fermion mass.
Gauge-equivariant flow models for sampling in lattice field theories with pseudofermions
This work presents gauge-equivariant architectures for flow-based sampling in fermionic lattice field theories using pseudofermions as stochastic estimators for the fermionic determinant.
Aspects of scaling and scalability for flow-based sampling of lattice QCD
Recent applications of machine-learned normalizing flows to sampling in lattice field theory suggest that such methods may be able to mitigate critical slowing down and topological freezing.
Laser: Latent Set Representations for 3D Generative Modeling
NeRF provides unparalleled fidelity of novel view synthesis: rendering a 3D scene from an arbitrary viewpoint.
Normalizing flows for lattice gauge theory in arbitrary space-time dimension
Applications of normalizing flows to the sampling of field configurations in lattice gauge theory have so far been explored almost exclusively in two space-time dimensions.
Advances in machine-learning-based sampling motivated by lattice quantum chromodynamics
This Perspective outlines the advances in ML-based sampling motivated by lattice quantum field theory, in particular for the theory of quantum chromodynamics.
Applications of flow models to the generation of correlated lattice QCD ensembles
Machine-learned normalizing flows can be used in the context of lattice quantum field theory to generate statistically correlated ensembles of lattice gauge fields at different action parameters.
Scaling Instructable Agents Across Many Simulated Worlds
Building embodied AI systems that can follow arbitrary language instructions in any 3D environment is a key challenge for creating general AI.
