Search Results for author:
Found 21 papers, 12 papers with code
Solving math word problems with process- and outcome-based feedback
Recent work has shown that asking language models to generate reasoning steps improves performance on many reasoning tasks.
Ranked #29 on
Arithmetic Reasoning
on GSM8K
(using extra training data)
Selection-Inference: Exploiting Large Language Models for Interpretable Logical Reasoning
Large language models (LLMs) have been shown to be capable of impressive few-shot generalisation to new tasks.
Symmetry-Based Representations for Artificial and Biological General Intelligence
In this review article we are going to argue that symmetry transformations are a fundamental principle that can guide our search for what makes a good representation.
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
Language modelling provides a step towards intelligent communication systems by harnessing large repositories of written human knowledge to better predict and understand the world.
Ranked #1 on
Language Modelling
on StackExchange
SyMetric: Measuring the Quality of Learnt Hamiltonian Dynamics Inferred from Vision
Using SyMetric, we identify a set of architectural choices that significantly improve the performance of a previously proposed model for inferring latent dynamics from pixels, the Hamiltonian Generative Network (HGN).
Which priors matter? Benchmarking models for learning latent dynamics
Learning dynamics is at the heart of many important applications of machine learning (ML), such as robotics and autonomous driving.
Representation Matters: Improving Perception and Exploration for Robotics
Furthermore, the value of each representation is evaluated in terms of three properties: dimensionality, observability and disentanglement.
Representation learning for improved interpretability and classification accuracy of clinical factors from EEG
Despite extensive standardization, diagnostic interviews for mental health disorders encompass substantial subjective judgment.
Disentangling by Subspace Diffusion
We present a novel nonparametric algorithm for symmetry-based disentangling of data manifolds, the Geometric Manifold Component Estimator (GEOMANCER).
Disentangled Cumulants Help Successor Representations Transfer to New Tasks
This is in contrast to the state-of-the-art reinforcement learning agents, which typically start learning each new task from scratch and struggle with knowledge transfer.
Hamiltonian Generative Networks
The Hamiltonian formalism plays a central role in classical and quantum physics.
Equivariant Hamiltonian Flows
This paper introduces equivariant hamiltonian flows, a method for learning expressive densities that are invariant with respect to a known Lie-algebra of local symmetry transformations while providing an equivariant representation of the data.
Unsupervised Model Selection for Variational Disentangled Representation Learning
Disentangled representations have recently been shown to improve fairness, data efficiency and generalisation in simple supervised and reinforcement learning tasks.
MONet: Unsupervised Scene Decomposition and Representation
The ability to decompose scenes in terms of abstract building blocks is crucial for general intelligence.
Towards a Definition of Disentangled Representations
Here we propose that a principled solution to characterising disentangled representations can be found by focusing on the transformation properties of the world.
Life-Long Disentangled Representation Learning with Cross-Domain Latent Homologies
Intelligent behaviour in the real-world requires the ability to acquire new knowledge from an ongoing sequence of experiences while preserving and reusing past knowledge.
Understanding disentangling in $β$-VAE
We present new intuitions and theoretical assessments of the emergence of disentangled representation in variational autoencoders.
DARLA: Improving Zero-Shot Transfer in Reinforcement Learning
Domain adaptation is an important open problem in deep reinforcement learning (RL).
SCAN: Learning Hierarchical Compositional Visual Concepts
SCAN learns concepts through fast symbol association, grounding them in disentangled visual primitives that are discovered in an unsupervised manner.
beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework
Learning an interpretable factorised representation of the independent data generative factors of the world without supervision is an important precursor for the development of artificial intelligence that is able to learn and reason in the same way that humans do.
Early Visual Concept Learning with Unsupervised Deep Learning
Automated discovery of early visual concepts from raw image data is a major open challenge in AI research.
