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Found 40 papers, 11 papers with code
Ascribing Consciousness to Artificial Intelligence
This paper critically assesses the anti-functionalist stance on consciousness adopted by certain advocates of integrated information theory (IIT), a corollary of which is that human-level artificial intelligence implemented on conventional computing hardware is necessarily not conscious.
Classifying Options for Deep Reinforcement Learning
In this paper we combine one method for hierarchical reinforcement learning - the options framework - with deep Q-networks (DQNs) through the use of different "option heads" on the policy network, and a supervisory network for choosing between the different options.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
Towards Deep Symbolic Reinforcement Learning
Deep reinforcement learning (DRL) brings the power of deep neural networks to bear on the generic task of trial-and-error learning, and its effectiveness has been convincingly demonstrated on tasks such as Atari video games and the game of Go.
Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders
We study a variant of the variational autoencoder model (VAE) with a Gaussian mixture as a prior distribution, with the goal of performing unsupervised clustering through deep generative models.
Ranked #7 on
Human Pose Forecasting
on HumanEva-I
Feature Control as Intrinsic Motivation for Hierarchical Reinforcement Learning
We highlight the advantage of our approach in one of the hardest games -- Montezuma's revenge -- for which the ability to handle sparse rewards is key.
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.
Continual Reinforcement Learning with Complex Synapses
Unlike humans, who are capable of continual learning over their lifetimes, artificial neural networks have long been known to suffer from a phenomenon known as catastrophic forgetting, whereby new learning can lead to abrupt erasure of previously acquired knowledge.
Relational Deep Reinforcement Learning
We introduce an approach for deep reinforcement learning (RL) that improves upon the efficiency, generalization capacity, and interpretability of conventional approaches through structured perception and relational reasoning.
Conditional Neural Processes
Deep neural networks excel at function approximation, yet they are typically trained from scratch for each new function.
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.
Explicit Information Placement on Latent Variables using Auxiliary Generative Modelling Task
We demonstrate the use of the method in a task of disentangling global structure from local features in images.
Policy Consolidation for Continual Reinforcement Learning
We propose a method for tackling catastrophic forgetting in deep reinforcement learning that is \textit{agnostic} to the timescale of changes in the distribution of experiences, does not require knowledge of task boundaries, and can adapt in \textit{continuously} changing environments.
Consistent Jumpy Predictions for Videos and Scenes
These models typically generate future frames in an autoregressive fashion, which is slow and requires the input and output frames to be consecutive.
Deep reinforcement learning with relational inductive biases
We introduce an approach for augmenting model-free deep reinforcement learning agents with a mechanism for relational reasoning over structured representations, which improves performance, learning efficiency, generalization, and interpretability.
An Explicitly Relational Neural Network Architecture
With a view to bridging the gap between deep learning and symbolic AI, we present a novel end-to-end neural network architecture that learns to form propositional representations with an explicitly relational structure from raw pixel data.
The Animal-AI Environment: Training and Testing Animal-Like Artificial Cognition
Recent advances in artificial intelligence have been strongly driven by the use of game environments for training and evaluating agents.
AlignNet: Self-supervised Alignment Module
The natural world consists of objects that we perceive as persistent in space and time, even though these objects appear, disappear and reappear in our field of view as we move.
Continual Reinforcement Learning with Multi-Timescale Replay
In this paper, we propose a multi-timescale replay (MTR) buffer for improving continual learning in RL agents faced with environments that are changing continuously over time at timescales that are unknown to the agent.
Systematic Generalisation through Task Temporal Logic and Deep Reinforcement Learning
This work introduces a neuro-symbolic agent that combines deep reinforcement learning (DRL) with temporal logic (TL) to achieve systematic zero-shot, i. e., never-seen-before, generalisation of formally specified instructions.
Learning Diverse Representations for Fast Adaptation to Distribution Shift
assumption is a useful idealization that underpins many successful approaches to supervised machine learning.
Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over Modules
To effectively utilize the wealth of potential top-down information available, and to prevent the cacophony of intermixed signals in a bidirectional architecture, mechanisms are needed to restrict information flow.
AlignNet: Unsupervised Entity Alignment
Recently developed deep learning models are able to learn to segment scenes into component objects without supervision.
Learning a Non-Redundant Collection of Classifiers
Inspired by Quality-Diversity algorithms, in this work we train a collection of classifiers to learn distinct solutions to a classification problem, with the goal of learning to exploit a variety of predictive signals present in the training data.
Unsupervised Object-Based Transition Models for 3D Partially Observable Environments
We present a slot-wise, object-based transition model that decomposes a scene into objects, aligns them (with respect to a slot-wise object memory) to maintain a consistent order across time, and predicts how those objects evolve over successive frames.
Encoders and Ensembles for Task-Free Continual Learning
We present an architecture that is effective for continual learning in an especially demanding setting, where task boundaries do not exist or are unknown, and where classes have to be learned online (with each example presented only once).
Learning to Represent State with Perceptual Schemata
We present empirical results that Perceptual Schemata enables a state representation that can maintain multiple objects observed in sequence with independent dynamics while an LSTM cannot.
Integrated information as a common signature of dynamical and information-processing complexity
The apparent dichotomy between information-processing and dynamical approaches to complexity science forces researchers to choose between two diverging sets of tools and explanations, creating conflict and often hindering scientific progress.
Ensembles and Encoders for Task-Free Continual Learning
We present an architecture that is effective for continual learning in an especially demanding setting, where task boundaries do not exist or are unknown, and where classes have to be learned online (with each presented only once).
Task-driven Discovery of Perceptual Schemas for Generalization in Reinforcement Learning
Taking inspiration from cognitive science, we term representations for reoccurring segments of an agent's experience, "perceptual schemas".
In a Nutshell, the Human Asked for This: Latent Goals for Following Temporal Specifications
We address the problem of building agents whose goal is to learn to execute out-of distribution (OOD) multi-task instructions expressed in temporal logic (TL) by using deep reinforcement learning (DRL).
Feature-Attending Recurrent Modules for Generalization in Reinforcement Learning
Many important tasks are defined in terms of object.
Abstraction for Deep Reinforcement Learning
We characterise the problem of abstraction in the context of deep reinforcement learning.
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.
Sparse Relational Reasoning with Object-Centric Representations
We investigate the composability of soft-rules learned by relational neural architectures when operating over object-centric (slot-based) representations, under a variety of sparsity-inducing constraints.
Faithful Reasoning Using Large Language Models
Although contemporary large language models (LMs) demonstrate impressive question-answering capabilities, their answers are typically the product of a single call to the model.
Talking About Large Language Models
Sitting squarely at the centre of this intersection are large language models (LLMs).
Role-Play with Large Language Models
As dialogue agents become increasingly human-like in their performance, it is imperative that we develop effective ways to describe their behaviour in high-level terms without falling into the trap of anthropomorphism.
Evaluating Large Language Model Creativity from a Literary Perspective
This paper assesses the potential for large language models (LLMs) to serve as assistive tools in the creative writing process, by means of a single, in-depth case study.
Improving Activation Steering in Language Models with Mean-Centring
Recent work in activation steering has demonstrated the potential to better control the outputs of Large Language Models (LLMs), but it involves finding steering vectors.
Simulacra as Conscious Exotica
The advent of conversational agents with increasingly human-like behaviour throws old philosophical questions into new light.
