126 citations · 457 across the 23 of their papers we have counts for
6 papers · 1 filter
Extrapolative Controlled Sequence Generation via Iterative Refinement
Vishakh Padmakumar, Richard Yuanzhe Pang, He He +1
We study the problem of extrapolative controlled generation, i.e., generating sequences with attribute values beyond the range seen in training. This task is of significant importa…
Amos: An Adam-style Optimizer with Adaptive Weight Decay towards Model-Oriented Scale
Ran Tian, Ankur P. Parikh
We present Amos, a stochastic gradient-based optimizer designed for training deep neural networks. It can be viewed as an Adam optimizer with theoretically supported, adaptive lear…
Consistency by Agreement in Zero-shot Neural Machine Translation
Maruan Al-Shedivat, Ankur P. Parikh
Generalization and reliability of multilingual translation often highly depend on the amount of available parallel data for each language pair of interest. In this paper, we focus…
Hybrid Subspace Learning for High-Dimensional Data
Micol Marchetti-Bowick, Benjamin J. Lengerich, Ankur P. Parikh +1
The high-dimensional data setting, in which p >> n, is a challenging statistical paradigm that appears in many real-world problems. In this setting, learning a compact, low-dimensi…
Infinite Mixed Membership Matrix Factorization
Avneesh Saluja, Mahdi Pakdaman, Dongzhen Piao +1
Rating and recommendation systems have become a popular application area for applying a suite of machine learning techniques. Current approaches rely primarily on probabilistic int…
A Spectral Algorithm for Latent Junction Trees
Ankur P. Parikh, Le Song, Mariya Ishteva +2
Latent variable models are an elegant framework for capturing rich probabilistic dependencies in many applications. However, current approaches typically parametrize these models u…