Bryan Singer

JMLR Journal 2002 Journal Article

Learning to Construct Fast Signal Processing Implementations

• Bryan Singer
• Manuela Veloso

A single signal processing algorithm can be represented by many mathematically equivalent formulas. However, when these formulas are implemented in code and run on real machines, they have very different runtimes. Unfortunately, it is extremely difficult to model this broad performance range. Further, the space of formulas for real signal transforms is so large that it is impossible to search it exhaustively for fast implementations. We approach this search question as a control learning problem. We present a new method for learning to generate fast formulas, allowing us to intelligently search through only the most promising formulas. Our approach incorporates signal processing knowledge, hardware features, and formula performance data to learn to construct fast formulas. Our method learns from performance data for a few formulas of one size and then can construct formulas that will have the fastest runtimes possible across many sizes.

ICML Conference 2001 Conference Paper

Learning to Generate Fast Signal Processing Implementations

• Bryan Singer
• Manuela Veloso

ICML Conference 2000 Conference Paper

Learning to Predict Performance from Formula Modeling and Training Data

• Bryan Singer
• Manuela Veloso

AAAI Conference 1999 Short Paper

Learning State Features from Policies to Bias Exploration in Reinforcement Learning

• Bryan Singer
• Manuela Veloso
• Carnegie Mellon University

When given several problems to solve in some domain, a standard reinforcement learner learns an optimal policy from scratch for each problem. This seems rather unfortunate in that one might expect some domain-specific information to be present in the solution to one problem for solving the next problem. Using this information would improve the reinforcement learner’s performance. However, policies learned by standard reinforcement learning techniques are often very dependent on the exact states, rewards, and state transitions in the particular problem. Therefore, it is infeasible to directly apply a learned policy to new problems, and so several approaches have been and are being investigated to find structure, abstraction, generalization, and/or policy reuse in reinforcement learning. Within our line of research, we describe each state in terms of local features, assuming that these state features together with the learned policies can be used to abstract out the domain characteristics from the specific layout of states and rewards of a particular problem. When given a new problem to solve, this abstraction is used as an exploration bias to improve the rate of convergence of a reinforcement learner.