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Found 10 papers, 3 papers with code
Monte Carlo inference for semiparametric Bayesian regression
Data transformations are essential for broad applicability of parametric regression models.
Semiparametric discrete data regression with Monte Carlo inference and prediction
These data commonly exhibit complex distributional features such as zero-inflation, over-/under-dispersion, boundedness, and heaping, which render many parametric models inadequate.
Example-Driven Model-Based Reinforcement Learning for Solving Long-Horizon Visuomotor Tasks
In this paper, we study the problem of learning a repertoire of low-level skills from raw images that can be sequenced to complete long-horizon visuomotor tasks.
Model-based Reinforcement Learning
reinforcement-learning
+1
On the Opportunities and Risks of Foundation Models
AI is undergoing a paradigm shift with the rise of models (e. g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.
Semiparametric count data regression for self-reported mental health
STAR is deployed to study the factors associated with self-reported mental health and demonstrates substantial improvements in goodness-of-fit compared to existing count data regression models.
Greedy Hierarchical Variational Autoencoders for Large-Scale Video Prediction
Our key insight is that greedy and modular optimization of hierarchical autoencoders can simultaneously address both the memory constraints and the optimization challenges of large-scale video prediction.
Ranked #1 on
Video Prediction
on Cityscapes 128x128
SQUIRL: Robust and Efficient Learning from Video Demonstration of Long-Horizon Robotic Manipulation Tasks
This paper aims to address this scalability challenge with a robust, sample-efficient, and general meta-IRL algorithm, SQUIRL, that performs a new but related long-horizon task robustly given only a single video demonstration.
MAT: Multi-Fingered Adaptive Tactile Grasping via Deep Reinforcement Learning
When this methodology is used to realize grasps from coarse initial positions provided by a vision-only planner, the system is made dramatically more robust to calibration errors in the camera-robot transform.
Pixel-Attentive Policy Gradient for Multi-Fingered Grasping in Cluttered Scenes
Recent advances in on-policy reinforcement learning (RL) methods enabled learning agents in virtual environments to master complex tasks with high-dimensional and continuous observation and action spaces.
Model Primitive Hierarchical Lifelong Reinforcement Learning
Learning interpretable and transferable subpolicies and performing task decomposition from a single, complex task is difficult.
