Search Results for author:
Found 15 papers, 5 papers with code
Physics-based machine learning for mantle convection simulations
Overall, our model is up to 89 times faster than the numerical solver.
Null Counterfactual Factor Interactions for Goal-Conditioned Reinforcement Learning
To leverage this intuition, we introduce Hindsight Relabeling using Interactions (HInt), which combines interactions with hindsight relabeling to improve the sample efficiency of downstream RL.
Reinforcement Learning Within the Classical Robotics Stack: A Case Study in Robot Soccer
Robot decision-making in partially observable, real-time, dynamic, and multi-agent environments remains a difficult and unsolved challenge.
RLZero: Direct Policy Inference from Language Without In-Domain Supervision
We further show that components of RLZero can be used to generate policies zero-shot from cross-embodied videos, such as those available on YouTube, even for complex embodiments like humanoids.
Proto Successor Measure: Representing the Space of All Possible Solutions of Reinforcement Learning
We present \emph{Proto Successor Measure}: the basis set for all possible solutions of Reinforcement Learning in a dynamical system.
Accelerating the discovery of steady-states of planetary interior dynamics with machine learning
Consequently, achieving steady-state requires a large number of time steps due to the disparate time scales governing the stagnant and convecting regions.
Aero-Nef: Neural Fields for Rapid Aircraft Aerodynamics Simulations
This paper presents a methodology to learn surrogate models of steady state fluid dynamics simulations on meshed domains, based on Implicit Neural Representations (INRs).
MemeMQA: Multimodal Question Answering for Memes via Rationale-Based Inferencing
Memes have evolved as a prevalent medium for diverse communication, ranging from humour to propaganda.
Robot Air Hockey: A Manipulation Testbed for Robot Learning with Reinforcement Learning
Reinforcement Learning is a promising tool for learning complex policies even in fast-moving and object-interactive domains where human teleoperation or hard-coded policies might fail.
$f$-Policy Gradients: A General Framework for Goal Conditioned RL using $f$-Divergences
We further introduce an entropy-regularized policy optimization objective, that we call $state$-MaxEnt RL (or $s$-MaxEnt RL) as a special case of our objective.
What do you MEME? Generating Explanations for Visual Semantic Role Labelling in Memes
Here, we introduce a novel task - EXCLAIM, generating explanations for visual semantic role labeling in memes.
Reinforcement Explanation Learning
Black-box methods to generate saliency maps are particularly interesting due to the fact that they do not utilize the internals of the model to explain the decision.
Deep learning for surrogate modelling of 2D mantle convection
Using a dataset of 10, 525 two-dimensional simulations of the thermal evolution of the mantle of a Mars-like planet, we show that deep learning techniques can produce reliable parameterized surrogates (i. e. surrogates that predict state variables such as temperature based only on parameters) of the underlying partial differential equations.
Learning to Deceive Knowledge Graph Augmented Models via Targeted Perturbation
Knowledge graphs (KGs) have helped neural models improve performance on various knowledge-intensive tasks, like question answering and item recommendation.
Poisoned classifiers are not only backdoored, they are fundamentally broken
Under this threat model, we propose a test-time, human-in-the-loop attack method to generate multiple effective alternative triggers without access to the initial backdoor and the training data.
