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Found 10 papers, 2 papers with code
Trust and ethical considerations in a multi-modal, explainable AI-driven chatbot tutoring system: The case of collaboratively solving Rubik's Cube
Artificial intelligence (AI) has the potential to transform education with its power of uncovering insights from massive data about student learning patterns.
On Solving the Rubik's Cube with Domain-Independent Planners Using Standard Representations
The fastest solver today for RC is DeepCubeA with a custom representation, and another approach is with Scorpion planner with State-Action-Space+ (SAS+) representation.
Independent Modular Networks
However, a lack of structure in each module's role, and modular network-specific issues such as module collapse have restricted their usability.
A* Search Without Expansions: Learning Heuristic Functions with Deep Q-Networks
We use Q* search to solve the Rubik's cube when formulated with a large action space that includes 1872 meta-actions and find that this 157-fold increase in the size of the action space incurs less than a 4-fold increase in computation time and less than a 3-fold increase in number of nodes generated when performing Q* search.
SPLASH: Learnable Activation Functions for Improving Accuracy and Adversarial Robustness
Furthermore, we show that SPLASH units significantly increase the robustness of deep neural networks to adversarial attacks.
Symmetric-APL Activations: Training Insights and Robustness to Adversarial Attacks
Finally, we show that the use of Symmetric-APL activations can significantly increase the robustness of deep neural networks to adversarial attacks.
Solving the Rubik's Cube with Approximate Policy Iteration
Autodidactic Iteration is able to learn how to solve the Rubik’s Cube and the 15-puzzle without relying on human data.
Solving the Rubik's Cube Without Human Knowledge
A generally intelligent agent must be able to teach itself how to solve problems in complex domains with minimal human supervision.
Learning Activation Functions to Improve Deep Neural Networks
Artificial neural networks typically have a fixed, non-linear activation function at each neuron.
Ranked #162 on
Image Classification
on CIFAR-10
Adaptive Multi-Column Deep Neural Networks with Application to Robust Image Denoising
Stacked sparse denoising auto-encoders (SSDAs) have recently been shown to be successful at removing noise from corrupted images.
