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Found 7 papers, 5 papers with code
Naturalistic Computational Cognitive Science: Towards generalizable models and theories that capture the full range of natural behavior
Artificial Intelligence increasingly pursues large, complex models that perform many tasks within increasingly realistic domains.
Predictive representations: building blocks of intelligence
Adaptive behavior often requires predicting future events.
Composing Task Knowledge with Modular Successor Feature Approximators
Recently, the Successor Features and Generalized Policy Improvement (SF&GPI) framework has been proposed as a method for learning, composing, and transferring predictive knowledge and behavior.
Feature-Attending Recurrent Modules for Generalization in Reinforcement Learning
Many important tasks are defined in terms of object.
Successor Feature Landmarks for Long-Horizon Goal-Conditioned Reinforcement Learning
SFL leverages the ability of successor features (SF) to capture transition dynamics, using it to drive exploration by estimating state-novelty and to enable high-level planning by abstracting the state-space as a non-parametric landmark-based graph.
Reinforcement Learning for Sparse-Reward Object-Interaction Tasks in a First-person Simulated 3D Environment
In this work, we show that one can learn object-interaction tasks from scratch without supervision by learning an attentive object-model as an auxiliary task during task learning with an object-centric relational RL agent.
Detecting Backdoor Attacks on Deep Neural Networks by Activation Clustering
While machine learning (ML) models are being increasingly trusted to make decisions in different and varying areas, the safety of systems using such models has become an increasing concern.
