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Found 15 papers, 6 papers with code
Artificial Intelligence for Prosthetics - challenge solutions
In the NeurIPS 2018 Artificial Intelligence for Prosthetics challenge, participants were tasked with building a controller for a musculoskeletal model with a goal of matching a given time-varying velocity vector.
Collaborative Evolutionary Reinforcement Learning
Deep reinforcement learning algorithms have been successfully applied to a range of challenging control tasks.
Evolutionary Reinforcement Learning for Sample-Efficient Multiagent Coordination
Training policies solely on the team-based reward is often difficult due to its sparsity.
Optimizing Memory Placement using Evolutionary Graph Reinforcement Learning
For deep neural network accelerators, memory movement is both energetically expensive and can bound computation.
Safety Aware Reinforcement Learning (SARL)
Since the safe agent effectively abstracts a task-independent notion of safety via its action probabilities, it can be ported to modulate multiple policies solving different tasks within the given environment without further training.
Learning Intrinsic Symbolic Rewards in Reinforcement Learning
We show that the discovered dense rewards are an effective signal for an RL policy to solve the benchmark tasks.
Dream and Search to Control: Latent Space Planning for Continuous Control
Learning and planning with latent space dynamics has been shown to be useful for sample efficiency in model-based reinforcement learning (MBRL) for discrete and continuous control tasks.
On Local Aggregation in Heterophilic Graphs
We also show that homophily is a poor measure of the information in a node's local neighborhood and propose the Neighborhood Information Content(NIC) metric, which is a novel information-theoretic graph metric.
Neuroevolution-Enhanced Multi-Objective Optimization for Mixed-Precision Quantization
In this work, we present a flexible and scalable framework for automated mixed-precision quantization that concurrently optimizes task performance, memory compression, and compute savings through multi-objective evolutionary computing.
Minimizing Communication while Maximizing Performance in Multi-Agent Reinforcement Learning
With these techniques, we show that we can reduce communication by 75% with no loss of performance.
Learning Spatial-Temporal Graphs for Active Speaker Detection
We address the problem of active speaker detection through a new framework, called SPELL, that learns long-range multimodal graphs to encode the inter-modal relationship between audio and visual data.
Exploiting Long-Term Dependencies for Generating Dynamic Scene Graphs
Dynamic scene graph generation from a video is challenging due to the temporal dynamics of the scene and the inherent temporal fluctuations of predictions.
Joint Hand Motion and Interaction Hotspots Prediction from Egocentric Videos
To tackle this task, we first provide an automatic way to collect trajectory and hotspots labels on large-scale data.
Learning Long-Term Spatial-Temporal Graphs for Active Speaker Detection
Active speaker detection (ASD) in videos with multiple speakers is a challenging task as it requires learning effective audiovisual features and spatial-temporal correlations over long temporal windows.
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Optimizing Multiagent Cooperation via Policy Evolution and Shared Experiences
Training policies solely on the team-based reward is often difficult due to its sparsity.
