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Found 11 papers, 3 papers with code
Tripod: Three Complementary Inductive Biases for Disentangled Representation Learning
Inductive biases are crucial in disentangled representation learning for narrowing down an underspecified solution set.
GenCHiP: Generating Robot Policy Code for High-Precision and Contact-Rich Manipulation Tasks
Large Language Models (LLMs) have been successful at generating robot policy code, but so far these results have been limited to high-level tasks that do not require precise movement.
What Makes Pre-Trained Visual Representations Successful for Robust Manipulation?
Inspired by the success of transfer learning in computer vision, roboticists have investigated visual pre-training as a means to improve the learning efficiency and generalization ability of policies learned from pixels.
Offline Reinforcement Learning at Multiple Frequencies
In this paper, we focus on one particular aspect of heterogeneity: learning from offline data collected at different control frequencies.
Neural Abstructions: Abstractions that Support Construction for Grounded Language Learning
Although virtual agents are increasingly situated in environments where natural language is the most effective mode of interaction with humans, these exchanges are rarely used as an opportunity for learning.
SPLAT: Semantic Pixel-Level Adaptation Transforms for Detection
Without dense labels, as is the case when only detection labels are available in the source, transformations are learned using CycleGAN alignment.
Exploiting Attention to Reveal Shortcomings in Memory Models
The decision making processes of deep networks are difficult to understand and while their accuracy often improves with increased architectural complexity, so too does their opacity.
Object Hallucination in Image Captioning
Despite continuously improving performance, contemporary image captioning models are prone to "hallucinating" objects that are not actually in a scene.
Evaluating Theory of Mind in Question Answering
We propose a new dataset for evaluating question answering models with respect to their capacity to reason about beliefs.
Women also Snowboard: Overcoming Bias in Captioning Models (Extended Abstract)
Most machine learning methods are known to capture and exploit biases of the training data.
Women also Snowboard: Overcoming Bias in Captioning Models
We introduce a new Equalizer model that ensures equal gender probability when gender evidence is occluded in a scene and confident predictions when gender evidence is present.
