Generalisation is a non-trivial problem in machine learning and more so with neural networks which have the capabilities of inducing varying degrees of freedom. It is influenced by many factors in network design, such as network size, initial conditions, learning rate, weight decay factor, pruning...
Thus, changes in the noise distribution between training and testing constitutes a crucial challenge to deep learning vision systems that can be systematically addressed in a lifelong machine learning approach. Our new dataset consisting of 83K carefully measured human psychophysical trials provide a ...
Learning and perceptual similarity among cuticular hydrocarbons in ants Generalisation depended both on the structure of the molecule and the animal's experience. For linear alkanes, generalisation was observed when the novel ... N Bos,S Dreier,CG J?Rgensen,... - 《Journal of Insect Physiology》...
The goal of this year's CBT is to generate versions of existing evaluation datasets for LLMs which, given a particular training corpus, have a larger distribution shift than the original test set, or – in other words – evaluate generalisation to a stronger degree than the original dataset. ...
We accept submissions that introduce new datasets, resplits of existing datasets along particular dimensions, or in-context learning tasks, with the goal of measuring generalisation of NLP models. We especially encourage submissions that focus on: ...
Explanation-based generalisation = partial evaluation作者: 摘要 We argue that explanation-based generalisation as recently proposed in the machine learning literature is essentially equivalent to partial evaluation, a well-known technique in the functional and logic programming literature. We show this ...
These compress an input signal from a given set of possible inputs onto a smaller number of bits, and are ‘work-horses’ of classical machine learning.2 Classical autoencoder Autoencoders are commonly achieved by a feedforward neural network with a bottleneck in the form of a layer with ...
(1989). Genetic Algorithms in Search, Optimization and Machine Learning. Massachusettes: Addison-Wesley. Haynes, T. D. & Wainwright, R. L. (1995). A Simulation of Adaptive Agents in Hostile Environment. In George, K. M., Carroll, J. H., Deaton, E., Oppenheim, D. & Hightower, J....
Using our method, recursive multiplying, we show reductions in execution time of between 8% and 40% of AllReduce on a Cray XC30 over recursive doubling. Using a custom simulator we further explore the dynamics of recursive multiplying 展开 关键词:...
Machine learningGeneralisationOverfittingData-driven modellingSupervised learning by means of Genetic Programming (GP) aims at the evolutionary synthesis of a model that achieves a balance between approximating the target function on the training data and generalising on new data. The model space searched...