Search Results for author: Ines Chami

Found 8 papers, 8 papers with code

Ask Me Anything: A simple strategy for prompting language models

3 code implementations5 Oct 2022 Simran Arora, Avanika Narayan, Mayee F. Chen, Laurel Orr, Neel Guha, Kush Bhatia, Ines Chami, Frederic Sala, Christopher Ré

Prompting is a brittle process wherein small modifications to the prompt can cause large variations in the model predictions, and therefore significant effort is dedicated towards designing a painstakingly "perfect prompt" for a task.

Coreference Resolution Natural Language Inference +2

Can Foundation Models Wrangle Your Data?

2 code implementations20 May 2022 Avanika Narayan, Ines Chami, Laurel Orr, Simran Arora, Christopher Ré

Foundation Models (FMs) are models trained on large corpora of data that, at very large scale, can generalize to new tasks without any task-specific finetuning.

Entity Resolution Imputation +1

HoroPCA: Hyperbolic Dimensionality Reduction via Horospherical Projections

1 code implementation7 Jun 2021 Ines Chami, Albert Gu, Dat Nguyen, Christopher Ré

Given directions, PCA relies on: (1) a parameterization of subspaces spanned by these directions, (2) a method of projection onto subspaces that preserves information in these directions, and (3) an objective to optimize, namely the variance explained by projections.

Dimensionality Reduction

From Trees to Continuous Embeddings and Back: Hyperbolic Hierarchical Clustering

2 code implementations NeurIPS 2020 Ines Chami, Albert Gu, Vaggos Chatziafratis, Christopher Ré

Recently, Dasgupta reframed HC as a discrete optimization problem by introducing a global cost function measuring the quality of a given tree.


Machine Learning on Graphs: A Model and Comprehensive Taxonomy

1 code implementation7 May 2020 Ines Chami, Sami Abu-El-Haija, Bryan Perozzi, Christopher Ré, Kevin Murphy

The second, graph regularized neural networks, leverages graphs to augment neural network losses with a regularization objective for semi-supervised learning.

BIG-bench Machine Learning Decoder +4

Hyperbolic Graph Convolutional Neural Networks

3 code implementations NeurIPS 2019 Ines Chami, Rex Ying, Christopher Ré, Jure Leskovec

Here we propose Hyperbolic Graph Convolutional Neural Network (HGCN), the first inductive hyperbolic GCN that leverages both the expressiveness of GCNs and hyperbolic geometry to learn inductive node representations for hierarchical and scale-free graphs.

 Ranked #1 on Link Prediction on PPI (Accuracy metric)

Link Prediction Node Classification

Referring Relationships

2 code implementations CVPR 2018 Ranjay Krishna, Ines Chami, Michael Bernstein, Li Fei-Fei

We formulate the cyclic condition between the entities in a relationship by modelling predicates that connect the entities as shifts in attention from one entity to another.

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