Ankitesh Gupta

Tom Beucler, Pierre Gentine, Janni Yuval et al.

Projecting climate change is a generalization problem: we extrapolate the recent past using physical models across past, present, and future climates. Current climate models require representations of processes that occur at scales smaller than model grid size, which have been the main source of model projection uncertainty. Recent machine learning (ML) algorithms hold promise to improve such process representations, but tend to extrapolate poorly to climate regimes they were not trained on. To get the best of the physical and statistical worlds, we propose a new framework - termed "climate-invariant" ML - incorporating knowledge of climate processes into ML algorithms, and show that it can maintain high offline accuracy across a wide range of climate conditions and configurations in three distinct atmospheric models. Our results suggest that explicitly incorporating physical knowledge into data-driven models of Earth system processes can improve their consistency, data efficiency, and generalizability across climate regimes.

Dharin Parekh, Ankitesh Gupta, Shharrnam Chhatpar et al.

Visual recognition of speech using the lip movement is called Lip-reading. Recent developments in this nascent field uses different neural networks as feature extractors which serve as input to a model which can map the temporal relationship and classify. Though end to end sentence level Lip-reading is the current trend, we proposed a new model which employs word level classification and breaks the set benchmarks for standard datasets. In our model we use convolutional autoencoders as feature extractors which are then fed to a Long short-term memory model. We tested our proposed model on BBC's LRW dataset, MIRACL-VC1 and GRID dataset. Achieving a classification accuracy of 98% on MIRACL-VC1 as compared to 93.4% of the set benchmark (Rekik et al., 2014). On BBC's LRW the proposed model performed better than the baseline model of convolutional neural networks and Long short-term memory model (Garg et al., 2016). Showing the features learned by the models we clearly indicate how the proposed model works better than the baseline model. The same model can also be extended for end to end sentence level classification.