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Found 7 papers, 1 papers with code
Weakly Supervised Learners for Correction of AI Errors with Provable Performance Guarantees
A key technical focus of the work is in providing performance guarantees for these new AI correctors through bounds on the probabilities of incorrect decisions.
Relative intrinsic dimensionality is intrinsic to learning
High dimensional data can have a surprising property: pairs of data points may be easily separated from each other, or even from arbitrary subsets, with high probability using just simple linear classifiers.
The Boundaries of Verifiable Accuracy, Robustness, and Generalisation in Deep Learning
We consider classical distribution-agnostic framework and algorithms minimising empirical risks and potentially subjected to some weights regularisation.
How adversarial attacks can disrupt seemingly stable accurate classifiers
We introduce a simple generic and generalisable framework for which key behaviours observed in practical systems arise with high probability -- notably the simultaneous susceptibility of the (otherwise accurate) model to easily constructed adversarial attacks, and robustness to random perturbations of the input data.
RWKV: Reinventing RNNs for the Transformer Era
This work presents a significant step towards reconciling trade-offs between computational efficiency and model performance in sequence processing tasks.
Ranked #22 on
Natural Language Inference
on WNLI
Quasi-orthogonality and intrinsic dimensions as measures of learning and generalisation
Recent work by Mellor et al (2021) showed that there may exist correlations between the accuracies of trained networks and the values of some easily computable measures defined on randomly initialised networks which may enable to search tens of thousands of neural architectures without training.
Demystification of Few-shot and One-shot Learning
Few-shot and one-shot learning have been the subject of active and intensive research in recent years, with mounting evidence pointing to successful implementation and exploitation of few-shot learning algorithms in practice.
