398 citations · 870 across the 15 of their papers we have counts for
18 papers
MEMORY-VQ: Compression for Tractable Internet-Scale Memory
Yury Zemlyanskiy, Michiel de Jong, Luke Vilnis +4
Retrieval augmentation is a powerful but expensive method to make language models more knowledgeable about the world. Memory-based methods like LUMEN pre-compute token representati…
ImPaKT: A Dataset for Open-Schema Knowledge Base Construction
Luke Vilnis, Zach Fisher, Bhargav Kanagal +2
Large language models have ushered in a golden age of semantic parsing. The seq2seq paradigm allows for open-schema and abstractive attribute and relation extraction given only sma…
Arithmetic Sampling: Parallel Diverse Decoding for Large Language Models
Luke Vilnis, Yury Zemlyanskiy, Patrick Murray +2
Decoding methods for large language models often trade-off between diversity of outputs and parallelism of computation. Methods such as beam search and Gumbel top-k sampling can gu…
Improving Local Identifiability in Probabilistic Box Embeddings
Shib Sankar Dasgupta, Michael Boratko, Dongxu Zhang +3
Geometric embeddings have recently received attention for their natural ability to represent transitive asymmetric relations via containment. Box embeddings, where objects are repr…
Embedded-State Latent Conditional Random Fields for Sequence Labeling
Dung Thai, Sree Harsha Ramesh, Shikhar Murty +2
Complex textual information extraction tasks are often posed as sequence labeling or \emph{shallow parsing}, where fields are extracted using local labels made consistent through p…
Hierarchical Losses and New Resources for Fine-grained Entity Typing and Linking
Shikhar Murty*, Patrick Verga*, Luke Vilnis +2
Extraction from raw text to a knowledge base of entities and fine-grained types is often cast as prediction into a flat set of entity and type labels, neglecting the rich hierarchi…
Probabilistic Embedding of Knowledge Graphs with Box Lattice Measures
Luke Vilnis, Xiang Li, Shikhar Murty +1
Embedding methods which enforce a partial order or lattice structure over the concept space, such as Order Embeddings (OE) (Vendrov et al., 2016), are a natural way to model transi…
Finer Grained Entity Typing with TypeNet
Shikhar Murty, Patrick Verga, Luke Vilnis +1
We consider the challenging problem of entity typing over an extremely fine grained set of types, wherein a single mention or entity can have many simultaneous and often hierarchic…
Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning
Rajarshi Das, Shehzaad Dhuliawala, Manzil Zaheer +5
Knowledge bases (KB), both automatically and manually constructed, are often incomplete --- many valid facts can be inferred from the KB by synthesizing existing information. A pop…
Distributional Inclusion Vector Embedding for Unsupervised Hypernymy Detection
Haw-Shiuan Chang, ZiYun Wang, Luke Vilnis +1
Modeling hypernymy, such as poodle is-a dog, is an important generalization aid to many NLP tasks, such as entailment, coreference, relation extraction, and question answering. Sup…
Low-Rank Hidden State Embeddings for Viterbi Sequence Labeling
Dung Thai, Shikhar Murty, Trapit Bansal +3
In textual information extraction and other sequence labeling tasks it is now common to use recurrent neural networks (such as LSTM) to form rich embedded representations of long-t…
Improved Representation Learning for Predicting Commonsense Ontologies
Xiang Li, Luke Vilnis, Andrew McCallum
Recent work in learning ontologies (hierarchical and partially-ordered structures) has leveraged the intrinsic geometry of spaces of learned representations to make predictions tha…
Adding Gradient Noise Improves Learning for Very Deep Networks
Arvind Neelakantan, Luke Vilnis, Quoc V. Le +4
Deep feedforward and recurrent networks have achieved impressive results in many perception and language processing applications. This success is partially attributed to architectu…
Generating Sentences from a Continuous Space
Samuel R. Bowman, Luke Vilnis, Oriol Vinyals +3
The standard recurrent neural network language model (RNNLM) generates sentences one word at a time and does not work from an explicit global sentence representation. In this work,…
Learning Dynamic Feature Selection for Fast Sequential Prediction
Emma Strubell, Luke Vilnis, Kate Silverstein +1
We present paired learning and inference algorithms for significantly reducing computation and increasing speed of the vector dot products in the classifiers that are at the heart…
Bethe Projections for Non-Local Inference
Luke Vilnis, David Belanger, Daniel Sheldon +1
Many inference problems in structured prediction are naturally solved by augmenting a tractable dependency structure with complex, non-local auxiliary objectives. This includes the…
Word Representations via Gaussian Embedding
Luke Vilnis, Andrew McCallum
Current work in lexical distributed representations maps each word to a point vector in low-dimensional space. Mapping instead to a density provides many interesting advantages, in…
Training for Fast Sequential Prediction Using Dynamic Feature Selection
Emma Strubell, Luke Vilnis, Andrew McCallum
We present paired learning and inference algorithms for significantly reducing computation and increasing speed of the vector dot products in the classifiers that are at the heart…