3 code implementations • 5 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.
Ranked #1 on Question Answering on Story Cloze
2 code implementations • 20 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.
Ranked #10 on Entity Resolution on Amazon-Google
1 code implementation • 7 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.
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.
1 code implementation • 7 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.
3 code implementations • ACL 2020 • Ines Chami, Adva Wolf, Da-Cheng Juan, Frederic Sala, Sujith Ravi, Christopher Ré
However, existing hyperbolic embedding methods do not account for the rich logical patterns in KGs.
Ranked #5 on Link Prediction on YAGO3-10
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)
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.