Luke Vilnis | DeepAI

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∙ 08/28/2023

MEMORY-VQ: Compression for Tractable Internet-Scale Memory

Retrieval augmentation is a powerful but expensive method to make langua...

0 Yury Zemlyanskiy, et al. ∙

∙ 12/21/2022

ImPaKT: A Dataset for Open-Schema Knowledge Base Construction

Large language models have ushered in a golden age of semantic parsing. ...

0 Luke Vilnis, et al. ∙

∙ 10/18/2022

Arithmetic Sampling: Parallel Diverse Decoding for Large Language Models

Decoding methods for large language models often trade-off between diver...

0 Luke Vilnis, et al. ∙

∙ 10/09/2020

Improving Local Identifiability in Probabilistic Box Embeddings

Geometric embeddings have recently received attention for their natural ...

3 Shib Sankar Dasgupta, et al. ∙

∙ 09/28/2018

Embedded-State Latent Conditional Random Fields for Sequence Labeling

Complex textual information extraction tasks are often posed as sequence...

2 Dung Thai, et al. ∙

∙ 07/13/2018

Hierarchical Losses and New Resources for Fine-grained Entity Typing and Linking

Extraction from raw text to a knowledge base of entities and fine-graine...

0 Shikhar Murty, et al. ∙

∙ 05/17/2018

Probabilistic Embedding of Knowledge Graphs with Box Lattice Measures

Embedding methods which enforce a partial order or lattice structure ove...

0 Luke Vilnis, et al. ∙

∙ 11/15/2017

Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning

Knowledge bases (KB), both automatically and manually constructed, are o...

0 Rajarshi Das, et al. ∙

∙ 11/15/2017

Finer Grained Entity Typing with TypeNet

We consider the challenging problem of entity typing over an extremely f...

0 Shikhar Murty, et al. ∙

∙ 10/02/2017

Unsupervised Hypernym Detection by Distributional Inclusion Vector Embedding

Modeling hypernymy, such as poodle is-a dog, is an important generalizat...

0 Haw-Shiuan Chang, et al. ∙

∙ 08/02/2017

Low-Rank Hidden State Embeddings for Viterbi Sequence Labeling

In textual information extraction and other sequence labeling tasks it i...

0 Dung Thai, et al. ∙

∙ 08/01/2017

Improved Representation Learning for Predicting Commonsense Ontologies

Recent work in learning ontologies (hierarchical and partially-ordered s...

0 Xiang Li, et al. ∙

∙ 11/21/2015

Adding Gradient Noise Improves Learning for Very Deep Networks

Deep feedforward and recurrent networks have achieved impressive results...

0 Arvind Neelakantan, et al. ∙

∙ 11/19/2015

Generating Sentences from a Continuous Space

The standard recurrent neural network language model (RNNLM) generates s...

0 Samuel R. Bowman, et al. ∙

∙ 03/04/2015

Bethe Projections for Non-Local Inference

Many inference problems in structured prediction are naturally solved by...

0 Luke Vilnis, et al. ∙

∙ 12/20/2014

Word Representations via Gaussian Embedding

Current work in lexical distributed representations maps each word to a ...

0 Luke Vilnis, et al. ∙

∙ 10/30/2014

Training for Fast Sequential Prediction Using Dynamic Feature Selection

We present paired learning and inference algorithms for significantly re...

0 Emma Strubell, et al. ∙

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