Search Results for author:
Found 11 papers, 6 papers with code
MAgent: A Many-Agent Reinforcement Learning Platform for Artificial Collective Intelligence
Unlike previous research platforms on single or multi-agent reinforcement learning, MAgent focuses on supporting the tasks and the applications that require hundreds to millions of agents.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Path-Level Network Transformation for Efficient Architecture Search
We introduce a new function-preserving transformation for efficient neural architecture search.
Learning to Design Circuits
We propose Learning to Design Circuits (L2DC) to leverage reinforcement learning that learns to efficiently generate new circuits data and to optimize circuits.
Towards Making the Most of BERT in Neural Machine Translation
Our experiments in machine translation show CTNMT gains of up to 3 BLEU score on the WMT14 English-German language pair which even surpasses the previous state-of-the-art pre-training aided NMT by 1. 4 BLEU score.
Park: An Open Platform for Learning-Augmented Computer Systems
We present Park, a platform for researchers to experiment with Reinforcement Learning (RL) for computer systems.
GCN-RL Circuit Designer: Transferable Transistor Sizing with Graph Neural Networks and Reinforcement Learning
Our transferable optimization method makes transistor sizing and design porting more effective and efficient.
OpenUE: An Open Toolkit of Universal Extraction from Text
We introduce a prototype model and provide an open-source and extensible toolkit called OpenUE for various extraction tasks.
Ranked #3 on
Joint Entity and Relation Extraction
on WebNLG
LOGEN: Few-shot Logical Knowledge-Conditioned Text Generation with Self-training
Previous works leverage logical forms to facilitate logical knowledge-conditioned text generation.
AOG-LSTM: An adaptive attention neural network for visual storytelling
Moreover, the existing method of alleviating error accumulation based on replacing reference words does not take into account the different effects of each word.
Ranked #20 on
Visual Storytelling
on VIST
Minuet: Accelerating 3D Sparse Convolutions on GPUs
Minuet proposes to (i) replace the hash tables used in the Map step with a novel segmented sorting double-traversed binary search algorithm that highly utilizes the on-chip memory hierarchy of GPUs, (ii) use a lightweight scheme to autotune the tile size in the Gather and Scatter operations of the GMaS step, such that to adapt the execution to the particular characteristics of each SC layer, dataset, and GPU architecture, and (iii) employ a padding-efficient GEMM grouping approach that reduces both memory padding and kernel launching overheads.
Accelerating Graph Neural Networks on Real Processing-In-Memory Systems
Graph Neural Network (GNN) execution involves both compute-intensive and memory-intensive kernels, the latter dominates the total time, being significantly bottlenecked by data movement between memory and processors.
