Factor Fitting, Rank Allocation, and Partitioning in Multilevel Low Rank Matrices

1 code implementation • 30 Oct 2023 • Tetiana Parshakova, Trevor Hastie, Eric Darve, Stephen Boyd

The second is rank allocation, where we choose the ranks of the blocks in each level, subject to the total rank having a given value, which preserves the total storage needed for the MLR matrix.

Efficient Graph Field Integrators Meet Point Clouds

1 code implementation • 2 Feb 2023 • Krzysztof Choromanski, Arijit Sehanobish, Han Lin, Yunfan Zhao, Eli Berger, Tetiana Parshakova, Alvin Pan, David Watkins, Tianyi Zhang, Valerii Likhosherstov, Somnath Basu Roy Chowdhury, Avinava Dubey, Deepali Jain, Tamas Sarlos, Snigdha Chaturvedi, Adrian Weller

We present two new classes of algorithms for efficient field integration on graphs encoding point clouds.

Distributional Reinforcement Learning for Energy-Based Sequential Models

1 code implementation • 18 Dec 2019 • Tetiana Parshakova, Jean-Marc Andreoli, Marc Dymetman

Global Autoregressive Models (GAMs) are a recent proposal [Parshakova et al., CoNLL 2019] for exploiting global properties of sequences for data-efficient learning of seq2seq models.

Distributional Reinforcement Learning reinforcement-learning +1

Global Autoregressive Models for Data-Efficient Sequence Learning

1 code implementation • CONLL 2019 • Tetiana Parshakova, Jean-Marc Andreoli, Marc Dymetman

In the second step, we use this GAM to train (by distillation) a second autoregressive model that approximates the \emph{normalized} distribution associated with the GAM, and can be used for fast inference and evaluation.

Language Modelling Small Data Image Classification