Search Results for author:
Found 25 papers, 9 papers with code
Test-Time Training for Generalization under Distribution Shifts
We introduce a general approach, called test-time training, for improving the performance of predictive models when training and test data come from different distributions.
Neural Borrowing Detection with Monolingual Lexical Models
From there we experiment with several different models and approaches including a lexical donor model with augmented wordlist.
Representation of Yine [Arawak] Morphology by Finite State Transducer Formalism
We represent the complexity of Yine (Arawak) morphology with a finite state transducer (FST) based morphological analyzer.
Multi-User Augmented Reality with Infrastructure-free Collaborative Localization
In this paper, we present LocAR, an infrastructure-free 6-degrees-of-freedom (6DoF) localization system for AR applications that uses motion estimates and range measurements between users to establish an accurate relative coordinate system.
Retiring Adult: New Datasets for Fair Machine Learning
Our primary contribution is a suite of new datasets derived from US Census surveys that extend the existing data ecosystem for research on fair machine learning.
Improving COVID-19 Forecasting using eXogenous Variables
We demonstrate significant enhancement in the forecasting accuracy for a COVID-19 dataset, with a maximum improvement in forecasting accuracy by 64. 58% and 59. 18% (on average) over the GCN-LSTM model in the national level data, and 58. 79% and 52. 40% (on average) over the GCN-LSTM model in the state level data.
Accuracy on the Line: On the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization
For machine learning systems to be reliable, we must understand their performance in unseen, out-of-distribution environments.
Outside the Echo Chamber: Optimizing the Performative Risk
In performative prediction, predictions guide decision-making and hence can influence the distribution of future data.
Gaussian Function On Response Surface Estimation
We utilize a Gaussian process as a surrogate to capture the response surface of a complex model, in which we incorporate two parts in the process: interpolated values that are modeled by a stationary Gaussian process Z governed by a prior covariance function, and a mean function mu that captures the known trends in the underlying model.
The Effect of Natural Distribution Shift on Question Answering Models
We build four new test sets for the Stanford Question Answering Dataset (SQuAD) and evaluate the ability of question-answering systems to generalize to new data.
A Meta-Analysis of Overfitting in Machine Learning
By systematically comparing the public ranking with the final ranking, we assess how much participants adapted to the holdout set over the course of a competition.
Strategic Classification is Causal Modeling in Disguise
Moreover, we show a similar result holds for designing cost functions that satisfy the requirements of previous work.
Test-Time Training with Self-Supervision for Generalization under Distribution Shifts
In this paper, we propose Test-Time Training, a general approach for improving the performance of predictive models when training and test data come from different distributions.
Test-Time Training for Out-of-Distribution Generalization
We introduce a general approach, called test-time training, for improving the performance of predictive models when test and training data come from different distributions.
Model Similarity Mitigates Test Set Overuse
Excessive reuse of test data has become commonplace in today's machine learning workflows.
Toward Universal Dependencies for Shipibo-Konibo
We present an initial version of the Universal Dependencies (UD) treebank for Shipibo-Konibo, the first South American, Amazonian, Panoan and Peruvian language with a resource built under UD.
The Social Cost of Strategic Classification
Consequential decision-making typically incentivizes individuals to behave strategically, tailoring their behavior to the specifics of the decision rule.
Stable Recurrent Models
Stability is a fundamental property of dynamical systems, yet to this date it has had little bearing on the practice of recurrent neural networks.
Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence Learning
We present Deep Voice 3, a fully-convolutional attention-based neural text-to-speech (TTS) system.
Globally Normalized Reader
Rapid progress has been made towards question answering (QA) systems that can extract answers from text.
Topic Model Stability for Hierarchical Summarization
We envisioned responsive generic hierarchical text summarization with summaries organized by section and paragraph based on hierarchical structure topic models.
Deep Voice 2: Multi-Speaker Neural Text-to-Speech
We introduce Deep Voice 2, which is based on a similar pipeline with Deep Voice 1, but constructed with higher performance building blocks and demonstrates a significant audio quality improvement over Deep Voice 1.
Deep Voice: Real-time Neural Text-to-Speech
We present Deep Voice, a production-quality text-to-speech system constructed entirely from deep neural networks.
Traversing Knowledge Graphs in Vector Space
Path queries on a knowledge graph can be used to answer compositional questions such as "What languages are spoken by people living in Lisbon?".
