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Found 13 papers, 2 papers with code
Active World Model Learning in Agent-rich Environments with Progress Curiosity
World models are a family of predictive models that solve self-supervised problems on how the world evolves.
Parsel: A (De-)compositional Framework for Algorithmic Reasoning with Language Models
Despite recent success in large language model (LLM) reasoning, LLMs struggle with hierarchical multi-step reasoning tasks like generating complex programs.
Ranked #1 on
Code Generation
on APPS
Interaction Modeling with Multiplex Attention
Modeling multi-agent systems requires understanding how agents interact.
Challenges and Opportunities for Machine Learning Classification of Behavior and Mental State from Images
Computer Vision (CV) classifiers which distinguish and detect nonverbal social human behavior and mental state can aid digital diagnostics and therapeutics for psychiatry and the behavioral sciences.
Training Affective Computer Vision Models by Crowdsourcing Soft-Target Labels
While the F1-score for a one-hot encoded classifier is much higher (94. 33% vs. 78. 68%) with respect to the ground truth CAFE labels, the output probability vector of the crowd-trained classifier more closely resembles the distribution of human labels (t=3. 2827, p=0. 0014).
Training an Emotion Detection Classifier using Frames from a Mobile Therapeutic Game for Children with Developmental Disorders
The classifier achieved 66. 9% balanced accuracy and 67. 4% F1-score on the entirety of CAFE as well as 79. 1% balanced accuracy and 78. 0% F1-score on CAFE Subset A, a subset containing at least 60% human agreement on emotions labels.
Active World Model Learning with Progress Curiosity
Humans learn world models by curiously exploring their environment, in the process acquiring compact abstractions of high bandwidth sensory inputs, the ability to plan across long temporal horizons, and an understanding of the behavioral patterns of other agents.
ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation
We introduce ThreeDWorld (TDW), a platform for interactive multi-modal physical simulation.
A Wearable Social Interaction Aid for Children with Autism
With most recent estimates giving an incidence rate of 1 in 68 children in the United States, the autism spectrum disorder (ASD) is a growing public health crisis.
Learning to Play With Intrinsically-Motivated, Self-Aware Agents
We demonstrate that this policy causes the agent to explore novel and informative interactions with its environment, leading to the generation of a spectrum of complex behaviors, including ego-motion prediction, object attention, and object gathering.
Flexible Neural Representation for Physics Prediction
Humans have a remarkable capacity to understand the physical dynamics of objects in their environment, flexibly capturing complex structures and interactions at multiple levels of detail.
Learning to Play with Intrinsically-Motivated Self-Aware Agents
We demonstrate that this policy causes the agent to explore novel and informative interactions with its environment, leading to the generation of a spectrum of complex behaviors, including ego-motion prediction, object attention, and object gathering.
Emergence of Structured Behaviors from Curiosity-Based Intrinsic Motivation
Moreover, the world model that the agent learns supports improved performance on object dynamics prediction and localization tasks.
