190 citations · 329 across the 14 of their papers we have counts for
14 papers
Contrastive Loss is All You Need to Recover Analogies as Parallel Lines
Narutatsu Ri, Fei-Tzin Lee, Nakul Verma
While static word embedding models are known to represent linguistic analogies as parallel lines in high-dimensional space, the underlying mechanism as to why they result in such g…
Improving Model Training via Self-learned Label Representations
Xiao Yu, Nakul Verma
Modern neural network architectures have shown remarkable success in several large-scale classification and prediction tasks. Part of the success of these architectures is their fl…
A Neural Network Solves, Explains, and Generates University Math Problems by Program Synthesis and Few-Shot Learning at Human Level
Iddo Drori, Sarah Zhang, Reece Shuttleworth +15
We demonstrate that a neural network pre-trained on text and fine-tuned on code solves mathematics course problems, explains solutions, and generates new questions at a human level…
An analysis of document graph construction methods for AMR summarization
Fei-Tzin Lee, Chris Kedzie, Nakul Verma +1
Meaning Representation (AMR) is a graph-based semantic representation for sentences, composed of collections of concepts linked by semantic relations. AMR-based approaches have fou…
Solving Probability and Statistics Problems by Program Synthesis
Leonard Tang, Elizabeth Ke, Nikhil Singh +2
We solve university level probability and statistics questions by program synthesis using OpenAI's Codex, a Transformer trained on text and fine-tuned on code. We transform course…
Solving Linear Algebra by Program Synthesis
Iddo Drori, Nakul Verma
We solve MIT's Linear Algebra 18.06 course and Columbia University's Computational Linear Algebra COMS3251 courses with perfect accuracy by interactive program synthesis. This surp…
Meta-Learning to Cluster
Yibo Jiang, Nakul Verma
Clustering is one of the most fundamental and wide-spread techniques in exploratory data analysis. Yet, the basic approach to clustering has not really changed: a practitioner hand…
Model-Agnostic Meta-Learning using Runge-Kutta Methods
Daniel Jiwoong Im, Yibo Jiang, Nakul Verma
Meta-learning has emerged as an important framework for learning new tasks from just a few examples. The success of any meta-learning model depends on (i) its fast adaptation to ne…
Metric Learning on Manifolds
Max Aalto, Nakul Verma
Recent literature has shown that symbolic data, such as text and graphs, is often better represented by points on a curved manifold, rather than in Euclidean space. However, geomet…
Noise-tolerant fair classification
Alexandre Louis Lamy, Ziyuan Zhong, Aditya Krishna Menon +1
Fairness-aware learning involves designing algorithms that do not discriminate with respect to some sensitive feature (e.g., race or gender). Existing work on the problem operates…
Stochastic Neighbor Embedding under f-divergences
Daniel Jiwoong Im, Nakul Verma, Kristin Branson
The t-distributed Stochastic Neighbor Embedding (t-SNE) is a powerful and popular method for visualizing high-dimensional data. It minimizes the Kullback-Leibler (KL) divergence be…
Sample complexity of learning Mahalanobis distance metrics
Nakul Verma, Kristin Branson
Metric learning seeks a transformation of the feature space that enhances prediction quality for the given task at hand. In this work we provide PAC-style sample complexity rates f…
A concentration theorem for projections
Sanjoy Dasgupta, Daniel Hsu, Nakul Verma
X in R^D has mean zero and finite second moments. We show that there is a precise sense in which almost all linear projections of X into R^d (for d < D) look like a scale-mixture o…
Which Spatial Partition Trees are Adaptive to Intrinsic Dimension?
Nakul Verma, Samory Kpotufe, Sanjoy Dasgupta
Recent theory work has found that a special type of spatial partition tree - called a random projection tree - is adaptive to the intrinsic dimension of the data from which it is b…