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Hi there 👋

I am a third year PhD student at ETH Zürich in Switzerland, supervised by Prof. Mrinmaya Sachan and Nicholas Monath from Google DeepMind. My PhD research investigate how generative LLMs reason, what factors affect their reasoning performance, and how to optimize their reasoning skills.

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Kumar Shridhar's Projects

arch_uncert icon arch_uncert

Code for "Variational Depth Search in ResNets" (https://arxiv.org/abs/2002.02797)

bayesian-neural-networks icon bayesian-neural-networks

Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace and more

big-bench icon big-bench

Beyond the Imitation Game collaborative benchmark for enormous language models

cv icon cv

My Curriculum vitae (CV)

distiiling-lm icon distiiling-lm

The code for the paper : Distilling Reasoning Capabilities into Smaller Language Models

dropouts icon dropouts

PyTorch Implementations of Dropout Variants

duckling icon duckling

Language, engine, and tooling for expressing, testing, and evaluating composable language rules on input strings.

examples icon examples

A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.

hall-of-fame icon hall-of-fame

:trophy: Show some love to your contributors! A widget for your repo README. Visual and clean. Refreshes every hour.

jointbert icon jointbert

Pytorch implementation of JointBERT: "BERT for Joint Intent Classification and Slot Filling"

know-your-intent icon know-your-intent

State of the Art results in Intent Classification using Sematic Hashing for three datasets: AskUbuntu, Chatbot and WebApplication.

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