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Found 12 papers, 0 papers with code
Some observations concerning Off Training Set (OTS) error
A form of generalisation error known as Off Training Set (OTS) error was recently introduced in [Wolpert, 1996b], along with a theorem showing that small training set error does not guarantee small OTS error, unless assumptions are made about the target function.
General Matrix-Matrix Multiplication Using SIMD features of the PIII
Generalised matrix-matrix multiplication forms the kernel of many mathematical algorithms.
Hebbian Synaptic Modifications in Spiking Neurons that Learn
In this paper, we derive a new model of synaptic plasticity, based on recent algorithms for reinforcement learning (in which an agent attempts to learn appropriate actions to maximize its long-term average reward).
Learning Model Bias
In this paper the problem of {\em learning} appropriate domain-specific bias is addressed.
A Bayesian/Information Theoretic Model of Bias Learning
In this paper the problem of learning appropriate bias for an environment of related tasks is examined from a Bayesian perspective.
The Canonical Distortion Measure for Vector Quantization and Function Approximation
In this paper it is shown how an {\em environment} of functions on an input space $X$ induces a {\em canonical distortion measure} (CDM) on X.
Learning Internal Representations (COLT 1995)
It is proved that the number of examples $m$ {\em per task} required to ensure good generalisation from a representation learner obeys $m = O(a+b/n)$ where $n$ is the number of tasks being learnt and $a$ and $b$ are constants.
92c/MFlops/s, Ultra-Large-Scale Neural-Network Training on a PIII Cluster
The training runs with a average performance of 163. 3 GFlops/s (single precision).
Learning Internal Representations (PhD Thesis)
In this thesis it is argued that in general there is insufficient information in a single task for a learner to generalise well and that what is required for good generalisation is information about many similar learning tasks.
Infinite-Horizon Policy-Gradient Estimation
In this paper we introduce GPOMDP, a simulation-based algorithm for generating a {\em biased} estimate of the gradient of the {\em average reward} in Partially Observable Markov Decision Processes (POMDPs) controlled by parameterized stochastic policies.
KnightCap: A chess program that learns by combining TD(lambda) with game-tree search
In this paper we present TDLeaf(lambda), a variation on the TD(lambda) algorithm that enables it to be used in conjunction with game-tree search.
