Navin Goyal | DeepAI

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∙ 06/19/2023

Guiding Language Models of Code with Global Context using Monitors

Language models of code (LMs) work well when the surrounding code in the...

0 Lakshya A Agrawal, et al. ∙

∙ 06/08/2023

In-Context Learning through the Bayesian Prism

In-context learning is one of the surprising and useful features of larg...

0 Kabir Ahuja, et al. ∙

∙ 03/14/2023

A Theory of Emergent In-Context Learning as Implicit Structure Induction

Scaling large language models (LLMs) leads to an emergent capacity to le...

1 Michael Hahn, et al. ∙

∙ 11/14/2022

Towards a Mathematics Formalisation Assistant using Large Language Models

Mathematics formalisation is the task of writing mathematics (i.e., defi...

0 Ayush Agrawal, et al. ∙

∙ 10/23/2022

When Can Transformers Ground and Compose: Insights from Compositional Generalization Benchmarks

Humans can reason compositionally whilst grounding language utterances t...

0 Ankur Sikarwar, et al. ∙

∙ 03/14/2022

Revisiting the Compositional Generalization Abilities of Neural Sequence Models

Compositional generalization is a fundamental trait in humans, allowing ...

0 Arkil Patel, et al. ∙

∙ 06/19/2021

Learning and Generalization in Overparameterized Normalizing Flows

In supervised learning, it is known that overparameterized neural networ...

0 Kulin Shah, et al. ∙

∙ 05/31/2021

Learning and Generalization in RNNs

Simple recurrent neural networks (RNNs) and their more advanced cousins ...

0 Abhishek Panigrahi, et al. ∙

∙ 04/29/2021

Analyzing the Nuances of Transformers' Polynomial Simplification Abilities

Symbolic Mathematical tasks such as integration often require multiple w...

0 Vishesh Agarwal, et al. ∙

∙ 03/12/2021

Are NLP Models really able to Solve Simple Math Word Problems?

The problem of designing NLP solvers for math word problems (MWP) has se...

0 Arkil Patel, et al. ∙

∙ 11/08/2020

On the Practical Ability of Recurrent Neural Networks to Recognize Hierarchical Languages

While recurrent models have been effective in NLP tasks, their performan...

0 Satwik Bhattamishra, et al. ∙

∙ 09/23/2020

On the Ability of Self-Attention Networks to Recognize Counter Languages

Transformers have supplanted recurrent models in a large number of NLP t...

0 Satwik Bhattamishra, et al. ∙

∙ 07/14/2020

Robust Identifiability in Linear Structural Equation Models of Causal Inference

In this work, we consider the problem of robust parameter estimation fro...

23 Karthik Abinav Sankararaman, et al. ∙

∙ 06/16/2020

On the Computational Power of Transformers and Its Implications in Sequence Modeling

Transformers are being used extensively across several sequence modeling...

0 Satwik Bhattamishra, et al. ∙

∙ 10/21/2019

Non-Gaussianity of Stochastic Gradient Noise

What enables Stochastic Gradient Descent (SGD) to achieve better general...

9 Abhishek Panigrahi, et al. ∙

∙ 08/16/2019

Effect of Activation Functions on the Training of Overparametrized Neural Nets

It is well-known that overparametrized neural networks trained using gra...

0 Abhishek Panigrahi, et al. ∙

∙ 07/24/2019

Sampling and Optimization on Convex Sets in Riemannian Manifolds of Non-Negative Curvature

The Euclidean space notion of convex sets (and functions) generalizes to...

0 Navin Goyal, et al. ∙

∙ 05/16/2019

Stability of Linear Structural Equation Models of Causal Inference

We consider the numerical stability of the parameter recovery problem in...

0 Karthik Abinav Sankararaman, et al. ∙

∙ 07/13/2018

Non-Gaussian Component Analysis using Entropy Methods

Non-Gaussian component analysis (NGCA) is a problem in multidimensional ...

0 Navin Goyal, et al. ∙

∙ 02/22/2017

Heavy-Tailed Analogues of the Covariance Matrix for ICA

Independent Component Analysis (ICA) is the problem of learning a square...

0 Joseph Anderson, et al. ∙

∙ 09/02/2015

Heavy-tailed Independent Component Analysis

Independent component analysis (ICA) is the problem of efficiently recov...

0 Joseph Anderson, et al. ∙

∙ 11/12/2013

The More, the Merrier: the Blessing of Dimensionality for Learning Large Gaussian Mixtures

In this paper we show that very large mixtures of Gaussians are efficien...

0 Joseph Anderson, et al. ∙

∙ 06/25/2013

Fourier PCA and Robust Tensor Decomposition

Fourier PCA is Principal Component Analysis of a matrix obtained from hi...

0 Navin Goyal, et al. ∙

∙ 11/09/2012

Efficient learning of simplices

We show an efficient algorithm for the following problem: Given uniforml...

0 Joseph Anderson, et al. ∙

∙ 09/15/2012

Further Optimal Regret Bounds for Thompson Sampling

Thompson Sampling is one of the oldest heuristics for multi-armed bandit...

0 Shipra Agrawal, et al. ∙

∙ 09/15/2012

Thompson Sampling for Contextual Bandits with Linear Payoffs

Thompson Sampling is one of the oldest heuristics for multi-armed bandit...

0 Shipra Agrawal, et al. ∙

∙ 06/07/2005

An Efficient Approximation Algorithm for Point Pattern Matching Under Noise

Point pattern matching problems are of fundamental importance in various...

0 Vicky Choi, et al. ∙

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