Zobrazeno 1 - 10
of 293
pro vyhledávání: '"Crowley, Mark"'
Learning causal representations from observational and interventional data in the absence of known ground-truth graph structures necessitates implicit latent causal representation learning. Implicit learning of causal mechanisms typically involves tw
Externí odkaz:
http://arxiv.org/abs/2402.11124
Reinforcement learning (RL) has been shown to learn sophisticated control policies for complex tasks including games, robotics, heating and cooling systems and text generation. The action-perception cycle in RL, however, generally assumes that a meas
Externí odkaz:
http://arxiv.org/abs/2307.02620
Autor:
Beeler, Chris, Subramanian, Sriram Ganapathi, Sprague, Kyle, Chatti, Nouha, Bellinger, Colin, Shahen, Mitchell, Paquin, Nicholas, Baula, Mark, Dawit, Amanuel, Yang, Zihan, Li, Xinkai, Crowley, Mark, Tamblyn, Isaac
This paper provides a simulated laboratory for making use of Reinforcement Learning (RL) for chemical discovery. Since RL is fairly data intensive, training agents `on-the-fly' by taking actions in the real world is infeasible and possibly dangerous.
Externí odkaz:
http://arxiv.org/abs/2305.14177
Conventional supervised learning methods typically assume i.i.d samples and are found to be sensitive to out-of-distribution (OOD) data. We propose Generative Causal Representation Learning (GCRL) which leverages causality to facilitate knowledge tra
Externí odkaz:
http://arxiv.org/abs/2302.08635
Multi-agent reinforcement learning typically suffers from the problem of sample inefficiency, where learning suitable policies involves the use of many data samples. Learning from external demonstrators is a possible solution that mitigates this prob
Externí odkaz:
http://arxiv.org/abs/2301.11153
Emotions can provide a natural communication modality to complement the existing multi-modal capabilities of social robots, such as text and speech, in many domains. We conducted three online studies with 112, 223, and 151 participants to investigate
Externí odkaz:
http://arxiv.org/abs/2208.09580
Publikováno v:
Proceedings of the 35th Canadian Conference on Artificial Intelligence, Canadian Artificial Intelligence Association, 2022
Locally Linear Embedding (LLE) is a nonlinear spectral dimensionality reduction and manifold learning method. It has two main steps which are linear reconstruction and linear embedding of points in the input space and embedding space, respectively. I
Externí odkaz:
http://arxiv.org/abs/2203.13911
Consider a set of $n$ data points in the Euclidean space $\mathbb{R}^d$. This set is called dataset in machine learning and data science. Manifold hypothesis states that the dataset lies on a low-dimensional submanifold with high probability. All dim
Externí odkaz:
http://arxiv.org/abs/2202.01619