Search Results for author: Jonathan Raiman

Found 13 papers, 7 papers with code

PrefixRL: Optimization of Parallel Prefix Circuits using Deep Reinforcement Learning

no code implementations14 May 2022 Rajarshi Roy, Jonathan Raiman, Neel Kant, Ilyas Elkin, Robert Kirby, Michael Siu, Stuart Oberman, Saad Godil, Bryan Catanzaro

Deep Convolutional RL agents trained on this environment produce prefix adder circuits that Pareto-dominate existing baselines with up to 16. 0% and 30. 2% lower area for the same delay in the 32b and 64b settings respectively.

reinforcement-learning Reinforcement Learning (RL)

Generative Adversarial Simulator

no code implementations23 Nov 2020 Jonathan Raiman

In this paper we introduce a simulator-free approach to knowledge distillation in the context of reinforcement learning.

Data-free Knowledge Distillation reinforcement-learning +1

Long-Term Planning and Situational Awareness in OpenAI Five

no code implementations13 Dec 2019 Jonathan Raiman, Susan Zhang, Filip Wolski

Understanding how knowledge about the world is represented within model-free deep reinforcement learning methods is a major challenge given the black box nature of its learning process within high-dimensional observation and action spaces.

Dota 2

Neural Network Surgery with Sets

no code implementations13 Dec 2019 Jonathan Raiman, Susan Zhang, Christy Dennison

The cost to train machine learning models has been increasing exponentially, making exploration and research into the correct features and architecture a costly or intractable endeavor at scale.

Dota 2

DeepType: Multilingual Entity Linking by Neural Type System Evolution

1 code implementation3 Feb 2018 Jonathan Raiman, Olivier Raiman

The wealth of structured (e. g. Wikidata) and unstructured data about the world available today presents an incredible opportunity for tomorrow's Artificial Intelligence.

Entity Disambiguation Entity Embeddings +3

Globally Normalized Reader

1 code implementation EMNLP 2017 Jonathan Raiman, John Miller

Rapid progress has been made towards question answering (QA) systems that can extract answers from text.

Data Augmentation Question Answering +2

Deep Voice 2: Multi-Speaker Neural Text-to-Speech

1 code implementation NeurIPS 2017 Sercan Arik, Gregory Diamos, Andrew Gibiansky, John Miller, Kainan Peng, Wei Ping, Jonathan Raiman, Yanqi Zhou

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.

Speech Synthesis

Occam's Gates

no code implementations27 Jun 2015 Jonathan Raiman, Szymon Sidor

We present a complimentary objective for training recurrent neural networks (RNN) with gating units that helps with regularization and interpretability of the trained model.

General Classification Question Answering +1

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