Inheritune builds smaller language models by reusing useful layers and reducing attention collapse, while retaining or improving performance.
We present a comprehensive review of self-supervised learning through the lens of information theory, introducing a unified framework that encompasses existing approaches and highlighting the interplay between compression and information preservation in deep neural networks.
We explored whether deep models should be a recommended option for tabular data by rigorously comparing the new deep models to XGBoost on various datasets. Our study shows that XGBoost outperforms these deep models across the datasets, including the datasets used in the papers that proposed the deep models. We also show that an ensemble of deep models and XGBoost performs better on these datasets than XGBoost alone.
Analysing perceptual learning of pure tones in the auditory cortex. Using a novel computational model, we show that overrepresentation of the learned tones does not improve along the training.
We extend the standard LSTM architecture by augmenting it with an additional gate which produces a memory control vector signal. This vector is fed back to the LSTM instead of the original output prediction. By decoupling the LSTM prediction from its role as a memory controller we allow each output to specialize in its own task.
We demonstrate the effectiveness of the Information-Plane visualization of DNNs. (i) Most of the training epochs are spent on compression of the input to efficient representation. (ii) The representation compression phase begins when the SGD steps change from a fast drift into a stochastic relaxation (iii) The converged layers lie very close to the information bottleneck theoretical bound, and the maps to the hidden layers satisfy the IB self-consistent equations (iv) The training time is dramatically reduced when adding more hidden layers.