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Found 13 papers, 7 papers with code
ChipNeMo: Domain-Adapted LLMs for Chip Design
ChipNeMo aims to explore the applications of large language models (LLMs) for industrial chip design.
PrefixRL: Optimization of Parallel Prefix Circuits using Deep Reinforcement Learning
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.
Generative Adversarial Simulator
In this paper we introduce a simulator-free approach to knowledge distillation in the context of reinforcement learning.
Dota 2 with Large Scale Deep Reinforcement Learning
On April 13th, 2019, OpenAI Five became the first AI system to defeat the world champions at an esports game.
Long-Term Planning and Situational Awareness in OpenAI Five
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.
Neural Network Surgery with Sets
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.
DeepType: Multilingual Entity Linking by Neural Type System Evolution
The wealth of structured (e. g. Wikidata) and unstructured data about the world available today presents an incredible opportunity for tomorrow's Artificial Intelligence.
Ranked #1 on
Entity Linking
on CoNLL-Aida
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.
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.
Deep Speech 2: End-to-End Speech Recognition in English and Mandarin
We show that an end-to-end deep learning approach can be used to recognize either English or Mandarin Chinese speech--two vastly different languages.
Occam's Gates
We present a complimentary objective for training recurrent neural networks (RNN) with gating units that helps with regularization and interpretability of the trained model.
