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Found 22 papers, 8 papers with code
Remembering Transformer for Continual Learning
Neural networks encounter the challenge of Catastrophic Forgetting (CF) in continual learning, where new task knowledge interferes with previously learned knowledge.
Stimulation technology for brain and nerves, now and future
In individuals afflicted with conditions such as paralysis, the implementation of Brain-Computer-Interface (BCI) has begun to significantly impact their quality of life.
Associative Transformer
Existing studies such as the Coordination method employ iterative cross-attention mechanisms with a bottleneck to enable the sparse association of inputs.
Consciousness in Artificial Intelligence: Insights from the Science of Consciousness
From these theories we derive "indicator properties" of consciousness, elucidated in computational terms that allow us to assess AI systems for these properties.
Logical Tasks for Measuring Extrapolation and Rule Comprehension
Furthermore, we discuss the relevance of logical tasks to concepts such as extrapolation, explainability, and inductive bias.
On the link between conscious function and general intelligence in humans and machines
In popular media, there is often a connection drawn between the advent of awareness in artificial agents and those same agents simultaneously achieving human or superhuman level intelligence.
AI agents for facilitating social interactions and wellbeing
While social relationships within groups are a critical factor for wellbeing, the development of wellbeing AI for social interactions remains relatively scarce.
Experimental Evidence that Empowerment May Drive Exploration in Sparse-Reward Environments
A possible countermeasure is to endow RL agents with an intrinsic reward function, or 'intrinsic motivation', which rewards the agent based on certain features of the current sensor state.
Deep Learning and the Global Workspace Theory
Recent advances in deep learning have allowed Artificial Intelligence (AI) to reach near human-level performance in many sensory, perceptual, linguistic or cognitive tasks.
Non-trivial informational closure of a Bayesian hyperparameter
On the other hand we attempt to establish a connection between a quantity that is a feature of the interpretation of the hyperparameter as a model, namely the information gain, and the one-step pointwise NTIC which is a quantity that does not depend on this interpretation.
A Technical Critique of Some Parts of the Free Energy Principle
Additionally, we highlight that the variational densities presented in newer formulations of the free energy principle and lemma are parameterised by different variables than in older works, leading to a substantially different interpretation of the theory.
A unified strategy for implementing curiosity and empowerment driven reinforcement learning
Curiosity reward informs the agent about the relevance of a recent agent action, whereas empowerment is implemented as the opposite information flow from the agent to the environment that quantifies the agent's potential of controlling its own future.
Boredom-driven curious learning by Homeo-Heterostatic Value Gradients
This paper presents the Homeo-Heterostatic Value Gradients (HHVG) algorithm as a formal account on the constructive interplay between boredom and curiosity which gives rise to effective exploration and superior forward model learning.
Being curious about the answers to questions: novelty search with learned attention
We investigate the use of attentional neural network layers in order to learn a `behavior characterization' which can be used to drive novelty search and curiosity-based policies.
Learning to generate classifiers
We train a network to generate mappings between training sets and classification policies (a 'classifier generator') by conditioning on the entire training set via an attentional mechanism.
Curiosity-driven reinforcement learning with homeostatic regulation
We propose a curiosity reward based on information theory principles and consistent with the animal instinct to maintain certain critical parameters within a bounded range.
Efficient Algorithms for Searching the Minimum Information Partition in Integrated Information Theory
In this study, we empirically explore to what extent the algorithm can be applied to the non-submodular measures of $\Phi$ by evaluating the accuracy of the algorithm in simulated data and real neural data.
Learning body-affordances to simplify action spaces
Controlling embodied agents with many actuated degrees of freedom is a challenging task.
A description length approach to determining the number of k-means clusters
Here we report two types of compression ratio based on two ways to quantify the description length of data after compression.
Counterfactual Control for Free from Generative Models
In this method, the problem of action selection is reduced to one of gradient descent on the latent space of the generative model, with the model itself providing the means of evaluating outcomes and finding the gradient, much like how the reward network in Deep Q-Networks (DQN) provides gradient information for the action generator.
Permutation-equivariant neural networks applied to dynamics prediction
The introduction of convolutional layers greatly advanced the performance of neural networks on image tasks due to innately capturing a way of encoding and learning translation-invariant operations, matching one of the underlying symmetries of the image domain.
Neural Coarse-Graining: Extracting slowly-varying latent degrees of freedom with neural networks
We present a loss function for neural networks that encompasses an idea of trivial versus non-trivial predictions, such that the network jointly determines its own prediction goals and learns to satisfy them.
