Search Results for author:
Found 9 papers, 5 papers with code
A Unified Framework for Factorizing Distributional Value Functions for Multi-Agent Reinforcement Learning
In fully cooperative multi-agent reinforcement learning (MARL) settings, environments are highly stochastic due to the partial observability of each agent and the continuously changing policies of other agents.
Ranked #1 on
SMAC
on SMAC 26m_vs_30m
Inductive Attention for Video Action Anticipation
Consequently, existing solutions based on the action recognition models are only suboptimal.
NVIDIA-UNIBZ Submission for EPIC-KITCHENS-100 Action Anticipation Challenge 2022
In this report, we describe the technical details of our submission for the EPIC-Kitchen-100 action anticipation challenge.
Unified Recurrence Modeling for Video Action Anticipation
To this end, we propose a unified recurrence modeling for video action anticipation via message passing framework.
Speech Representation Learning Through Self-supervised Pretraining And Multi-task Finetuning
It has been shown that an SSL pretraining model can achieve excellent performance in various downstream tasks of speech processing.
SpeechNet: A Universal Modularized Model for Speech Processing Tasks
The multi-task learning of a wide variety of speech processing tasks with a universal model has not been studied.
Higher Order Recurrent Space-Time Transformer for Video Action Prediction
Endowing visual agents with predictive capability is a key step towards video intelligence at scale.
DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning
In fully cooperative multi-agent reinforcement learning (MARL) settings, the environments are highly stochastic due to the partial observability of each agent and the continuously changing policies of the other agents.
Ranked #1 on
SMAC
on SMAC 27m_vs_30m
DARTS-ASR: Differentiable Architecture Search for Multilingual Speech Recognition and Adaptation
In order to examine the generalizability of DARTS-ASR, we apply our approach not only on many languages to perform monolingual ASR, but also on a multilingual ASR setting.
