Search Results for author: S. Karthik Mukkavilli

Found 9 papers, 2 papers with code

TensorBank: Tensor Lakehouse for Foundation Model Training

no code implementations5 Sep 2023 Romeo Kienzler, Leonardo Pondian Tizzei, Benedikt Blumenstiel, Zoltan Arnold Nagy, S. Karthik Mukkavilli, Johannes Schmude, Marcus Freitag, Michael Behrendt, Daniel Salles Civitarese, Naomi Simumba, Daiki Kimura, Hendrik Hamann

Storing and streaming high dimensional data for foundation model training became a critical requirement with the rise of foundation models beyond natural language.

AB2CD: AI for Building Climate Damage Classification and Detection

no code implementations3 Sep 2023 Maximilian Nitsche, S. Karthik Mukkavilli, Niklas Kühl, Thomas Brunschwiler

To achieve robust and accurate evaluations of building damage detection and classification, we evaluated different deep learning models with residual, squeeze and excitation, and dual path network backbones, as well as ensemble techniques.


Predicting ice flow using machine learning

no code implementations20 Oct 2019 Yimeng Min, S. Karthik Mukkavilli, Yoshua Bengio

Though machine learning has achieved notable success in modeling sequential and spatial data for speech recognition and in computer vision, applications to remote sensing and climate science problems are seldom considered.

BIG-bench Machine Learning Management +3

Deep learning for Aerosol Forecasting

no code implementations14 Oct 2019 Caleb Hoyne, S. Karthik Mukkavilli, David Meger

Reanalysis datasets combining numerical physics models and limited observations to generate a synthesised estimate of variables in an Earth system, are prone to biases against ground truth.

Visualizing the Consequences of Climate Change Using Cycle-Consistent Adversarial Networks

no code implementations2 May 2019 Victor Schmidt, Alexandra Luccioni, S. Karthik Mukkavilli, Narmada Balasooriya, Kris Sankaran, Jennifer Chayes, Yoshua Bengio

We present a project that aims to generate images that depict accurate, vivid, and personalized outcomes of climate change using Cycle-Consistent Adversarial Networks (CycleGANs).

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