Nurendra Choudhary @nurendra_c
Ph.D. Candidate @VT_CS | Applied Science Intern @AmazonScience | ex-Analyst @GoldmanSachs nurendra.me Bay Area, California Joined February 2015-
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It is a real pain to be constrained by the 2K context window in LLaMA models. In our arXiv, we extend the context up to 32K with <1000 fine-tuning steps and largely keep performance, by *interpolating* positional encoding, rather than extrapolating it. We also give a theoretical…
Wonder where our students @SanghaniCtrVT go after earning a Ph.D. @VT_CS? Dr. Nurendra Choudhary (@nurendra_c), here with advisor @chandankreddy @VTmetroDC commencement, tells us he is joining Amazon in Palo Alto, CA, as an applied scientist II. Congrats! 🎉 #HokieGrad #VT23
Ever wanted a Gaussian process whose domain is a manifold important enough to actually have a name? If it's a Lie group or homogeneous space, we've worked out a general recipe for computing sq. exp. (heat, diffusion, ..) and Matérn kernels on it! arxiv.org/abs/2208.14960
Presenting our work “Graph-based Multilingual Language Model: Leveraging Product Relations for Search Relevance” in room 202B at 1.30PM today at #KDD2022. Please join us if you are interested in graph-based search. @AmazonScience @SanghaniCtrVT.
Excited about hyperbolic networks and want a crash course to them? Attend our tutorial Hyperbolic Neural Networks: Theory, Architectures and Applications @kdd_news #KDD2022 in Room 209C at 1 pm EDT today. @AmazonScience @SanghaniCtrVT
Need a scalable approach to improve your search results. How about integrating your knowledge graphs with the queries? Take a look at our paper on Graph-based LM at #KDD2022. Paper: bit.ly/graphlm-kdd2022 Code: bit.ly/graphlm-code @AmazonScience @SanghaniCtrVT @kdd_news
Excited to announce our model ANTHEM at #WSDM22 We propose a new method that utilizes hyperbolic space to answer natural language queries over the product catalogue. Hope you like it!! @nikhilrao86 @sumeetwisc Karthik Subbian @chandankreddy@AmazonScience amazon.science/publications/a…
Excited for our work on Gaussian KG embeddings "Probabilistic Entity Representation Model for Chain Reasoning over Knowledge Graphs" which got accepted at #NeurIPS2021 @nikhilrao86 @sumeetwisc Karthik Subbian @chandankreddy arxiv.org/abs/2110.13522
Graph neural networks are driving lots of progress in machine learning by extending deep learning approaches to complex graph data and applications. Let’s take a look at a few methods ↓
At @TheWebConf, Amazon scientists presented a new way to embed knowledge graphs using hyperboloids — curved analogues of rectangles — that enables a 7% to 33% improvement over its best-performing predecessors in handling graph queries. #TheWebConf #WWW2021 amzn.to/3h9RKww
Ph.D. student @nurendra_c was drawn to @SanghaniCtrVT because his research interests aligned with now advisor @chandankreddy. He says the center has helped him broaden his work on deep learning methods in information retrieval. Read his student spotlight: bit.ly/3vsewDI
My favourite irony of AI is that playing chess turns out to be fairly easy ... It's kicking a football or having a chat with your neighbour that turns out to be very hard.
Today @TheWebConf Ph.D. student @nurendra_c @VT_CS @SanghaniCtrVT presented "Self-Supervised Hyperboloid Representations Logical Queries over Knowledge Graphs," his work w/@nikhilrao86 @sumeetwisc & Karthik Subbian @AmazonScience and @chandankreddy. bit.ly/3asWkSr
Thrilled that "Self-Supervised Hyperboloid Representations from Logical Queries over Knowledge Graphs" with @nikhilrao86 @sumeetwisc Karthik Subbian @chandankreddy got accepted at The Web Conference. #TheWebConf2021 #www2021 Preprint: arxiv.org/abs/2012.13023

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