Machine Learning and AI | India
Projets
Our work in machine learning theory ranges from modeling to mathematical analysis of algorithms to experimental probing. The diverse set of topics include clustering, optimization, unsupervised learning, algebraic methods, fairness, causality, and deep learning.
In this area of research, we broadly explore combining machine learning and program synthesis in various ways. This is an umbrella project that has spawned several projects exploring applications of such a combination in different areas. Heterogeneous data extraction framework…
High-stakes decision-making in areas like healthcare, finance and governance requires accountability for decisions and for how data is used in making decisions. Many concerns have been raised about whether machine learning (ML) models can meet these expectations. In many cases,…
xtreme classification is a rapidly growing research area in computer vision focusing on multi-class and multi-label problems involving an extremely large number of labels (ranging from thousands to billions).
In this project, we present a way to combine techniques from the program synthesis and machine learning communities to extract structured information from heterogeneous data. Such problems arise in several situations such as extracting attributes from web pages, machine-generated emails,…
Établi:
Large Language Models meet Program Synthesis Large pre-trained language models such as GPT-3, Codex, and Google’s language model are now capable of generating code from natural language specifications of programmer intent. We view these developments with a mixture of optimism…
Établi:
The goal of Project LITMUS is to discover strategies to evaluate massive multilingual models and also suggest data collection and training strategies to improve the performance of these models.
Our objective is to develop a library of efficient machine learning algorithms that can run on severely resource-constrained edge and endpoint IoT devices ranging from the Arduino to the Raspberry Pi.
Établi:
We design algorithms to address the challenges of scaling ANNS for web and enterprise search and recommendation systems. Our goal is to build systems that serve trillions of points in a streaming setting cost effectively.