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Found 12 papers, 3 papers with code
DE-HNN: An effective neural model for Circuit Netlist representation
Using the input and output data of the tools from past designs, one can attempt to build a machine learning model that predicts the outcome of a design in significantly shorter time than running the tool.
Less is More: Hop-Wise Graph Attention for Scalable and Generalizable Learning on Circuits
In this work we propose HOGA, a novel attention-based model for learning circuit representations in a scalable and generalizable manner.
Human Control: Definitions and Algorithms
How can humans stay in control of advanced artificial intelligence systems?
Reasoning about Causality in Games
Regarding question iii), we describe correspondences between causal games and other formalisms, and explain how causal games can be used to answer queries that other causal or game-theoretic models do not support.
Path-Specific Objectives for Safer Agent Incentives
We then train agents to maximize the causal effect of actions on the expected return which is not mediated by the delicate parts of state, using Causal Influence Diagram analysis.
Too Big to Fail? Active Few-Shot Learning Guided Logic Synthesis
Generating sub-optimal synthesis transformation sequences ("synthesis recipe") is an important problem in logic synthesis.
A Complete Criterion for Value of Information in Soluble Influence Diagrams
Influence diagrams have recently been used to analyse the safety and fairness properties of AI systems.
Why Fair Labels Can Yield Unfair Predictions: Graphical Conditions for Introduced Unfairness
In addition to reproducing discriminatory relationships in the training data, machine learning systems can also introduce or amplify discriminatory effects.
Agent Incentives: A Causal Perspective
We propose a new graphical criterion for value of control, establishing its soundness and completeness.
The Incentives that Shape Behaviour
Which variables does an agent have an incentive to control with its decision, and which variables does it have an incentive to respond to?
(When) Is Truth-telling Favored in AI Debate?
For some problems, humans may not be able to accurately judge the goodness of AI-proposed solutions.
Incorrigibility in the CIRL Framework
We demonstrate this by presenting some Supervised POMDP scenarios in which errors in the parameterized reward function remove the incentive to follow shutdown commands.
