Search Results for author:
Found 9 papers, 3 papers with code
Language models scale reliably with over-training and on downstream tasks
We fit scaling laws that extrapolate in both the number of model parameters and the ratio of training tokens to parameters.
Residual Q-Learning: Offline and Online Policy Customization without Value
Specifically, we formulate the customization problem as a Markov Decision Process (MDP) with a reward function that combines 1) the inherent reward of the demonstration; and 2) the add-on reward specified by the downstream task.
RAP: Risk-Aware Prediction for Robust Planning
Robust planning in interactive scenarios requires predicting the uncertain future to make risk-aware decisions.
Control-Aware Prediction Objectives for Autonomous Driving
Autonomous vehicle software is typically structured as a modular pipeline of individual components (e. g., perception, prediction, and planning) to help separate concerns into interpretable sub-tasks.
Dynamics-Aware Comparison of Learned Reward Functions
The ability to learn reward functions plays an important role in enabling the deployment of intelligent agents in the real world.
Higher Order Linear Transformer
Following up on the linear transformer part of the article from Katharopoulos et al., that takes this idea from Shen et al., the trick that produces a linear complexity for the attention mechanism is re-used and extended to a second-order approximation of the softmax normalization.
Social Attention for Autonomous Decision-Making in Dense Traffic
We study the design of learning architectures for behavioural planning in a dense traffic setting.
Multi-Head Attention for Multi-Modal Joint Vehicle Motion Forecasting
This paper presents a novel vehicle motion forecasting method based on multi-head attention.
Kinematic Single Vehicle Trajectory Prediction Baselines and Applications with the NGSIM Dataset
This article is meant to be used along with the published code to establish baselines for further work.
