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Found 13 papers, 6 papers with code
Effective Long-Context Scaling of Foundation Models
We also examine the impact of various design choices in the pretraining process, including the data mix and the training curriculum of sequence lengths -- our ablation experiments suggest that having abundant long texts in the pretrain dataset is not the key to achieving strong performance, and we empirically verify that long context continual pretraining is more efficient and similarly effective compared to pretraining from scratch with long sequences.
Surrogate Assisted Generation of Human-Robot Interaction Scenarios
In the shared control teleoperation domain and a more complex shared workspace collaboration task, we show that surrogate assisted scenario generation efficiently synthesizes diverse datasets of challenging scenarios.
PATO: Policy Assisted TeleOperation for Scalable Robot Data Collection
Large-scale data is an essential component of machine learning as demonstrated in recent advances in natural language processing and computer vision research.
Video Game Level Repair via Mixed Integer Linear Programming
Recent advancements in procedural content generation via machine learning enable the generation of video-game levels that are aesthetically similar to human-authored examples.
Incorporating structured assumptions with probabilistic graphical models in fMRI data analysis
With the wide adoption of functional magnetic resonance imaging (fMRI) by cognitive neuroscience researchers, large volumes of brain imaging data have been accumulated in recent years.
Zero-Shot Imitating Collaborative Manipulation Plans from YouTube Cooking Videos
Building upon this theory of language for action, we propose a system for understanding and executing demonstrated action sequences from full-length, real-world cooking videos on the web.
Interactive Differentiable Simulation
While learning-based models of the environment dynamics have contributed to significant improvements in sample efficiency compared to model-free reinforcement learning algorithms, they typically fail to generalize to system states beyond the training data, while often grounding their predictions on non-interpretable latent variables.
Simulator Predictive Control: Using Learned Task Representations and MPC for Zero-Shot Generalization and Sequencing
We complete unseen tasks by choosing new sequences of skill latents to control the robot using MPC, where our MPC model is composed of the pre-trained skill policy executed in the simulation environment, run in parallel with the real robot.
Auto-conditioned Recurrent Mixture Density Networks for Learning Generalizable Robot Skills
Personal robots assisting humans must perform complex manipulation tasks that are typically difficult to specify in traditional motion planning pipelines, where multiple objectives must be met and the high-level context be taken into consideration.
Scaling simulation-to-real transfer by learning composable robot skills
In particular, we first use simulation to jointly learn a policy for a set of low-level skills, and a "skill embedding" parameterization which can be used to compose them.
Segmenting Brain Tumors with Symmetry
We explore encoding brain symmetry into a neural network for a brain tumor segmentation task.
A Searchlight Factor Model Approach for Locating Shared Information in Multi-Subject fMRI Analysis
In this work, we examine a searchlight based shared response model to identify shared information in small contiguous regions (searchlights) across the whole brain.
A Convolutional Autoencoder for Multi-Subject fMRI Data Aggregation
We examine two ways to combine the ideas of a factor model and a searchlight based analysis to aggregate multi-subject fMRI data while preserving spatial locality.
