518 citations · 948 across the 34 of their papers we have counts for
17 papers · 1 filter
In-Context Learning through the Bayesian Prism
Madhur Panwar, Kabir Ahuja, Navin Goyal
In-context learning (ICL) is one of the surprising and useful features of large language models and subject of intense research. Recently, stylized meta-learning-like ICL setups ha…
Learning and Generalization in Overparameterized Normalizing Flows
Kulin Shah, Amit Deshpande, Navin Goyal
In supervised learning, it is known that overparameterized neural networks with one hidden layer provably and efficiently learn and generalize, when trained using stochastic gradie…
Learning and Generalization in RNNs
Abhishek Panigrahi, Navin Goyal
Simple recurrent neural networks (RNNs) and their more advanced cousins LSTMs etc. have been very successful in sequence modeling. Their theoretical understanding, however, is lack…
Robust Identifiability in Linear Structural Equation Models of Causal Inference
Karthik Abinav Sankararaman, Anand Louis, Navin Goyal
In this work, we consider the problem of robust parameter estimation from observational data in the context of linear structural equation models (LSEMs). LSEMs are a popular and we…
On the Computational Power of Transformers and its Implications in Sequence Modeling
Satwik Bhattamishra, Arkil Patel, Navin Goyal
Transformers are being used extensively across several sequence modeling tasks. Significant research effort has been devoted to experimentally probe the inner workings of Transform…
Non-Gaussianity of Stochastic Gradient Noise
Abhishek Panigrahi, Raghav Somani, Navin Goyal +1
What enables Stochastic Gradient Descent (SGD) to achieve better generalization than Gradient Descent (GD) in Neural Network training? This question has attracted much attention. I…