Zobrazeno 1 - 10
of 124
pro vyhledávání: '"Ramadge, Peter J."'
We introduce marginalization models (MAMs), a new family of generative models for high-dimensional discrete data. They offer scalable and flexible generative modeling by explicitly modeling all induced marginal distributions. Marginalization models e
Externí odkaz:
http://arxiv.org/abs/2310.12920
Safety is a central requirement for autonomous system operation across domains. Hamilton-Jacobi (HJ) reachability analysis can be used to construct "least-restrictive" safety filters that result in infrequent, but often extreme, control overrides. In
Externí odkaz:
http://arxiv.org/abs/2307.00193
The use of positional embeddings in transformer language models is widely accepted. However, recent research has called into question the necessity of such embeddings. We further extend this inquiry by demonstrating that a randomly initialized and fr
Externí odkaz:
http://arxiv.org/abs/2305.13571
Unlike recurrent models, conventional wisdom has it that Transformers cannot perfectly model regular languages. Inspired by the notion of working memory, we propose a new Transformer variant named RegularGPT. With its novel combination of Weight-Shar
Externí odkaz:
http://arxiv.org/abs/2305.03796
Length extrapolation permits training a transformer language model on short sequences that preserves perplexities when tested on substantially longer sequences. A relative positional embedding design, ALiBi, has had the widest usage to date. We disse
Externí odkaz:
http://arxiv.org/abs/2212.10356
While deep generative models have succeeded in image processing, natural language processing, and reinforcement learning, training that involves discrete random variables remains challenging due to the high variance of its gradient estimation process
Externí odkaz:
http://arxiv.org/abs/2206.07235
Relative positional embeddings (RPE) have received considerable attention since RPEs effectively model the relative distance among tokens and enable length extrapolation. We propose KERPLE, a framework that generalizes relative position embedding for
Externí odkaz:
http://arxiv.org/abs/2205.09921
Safety-critical applications require controllers/policies that can guarantee safety with high confidence. The control barrier function is a useful tool to guarantee safety if we have access to the ground-truth system dynamics. In practice, we have in
Externí odkaz:
http://arxiv.org/abs/2112.12210
Autor:
Fan, Ting-Han, Ramadge, Peter J.
Off-policy Actor-Critic algorithms have demonstrated phenomenal experimental performance but still require better explanations. To this end, we show its policy evaluation error on the distribution of transitions decomposes into: a Bellman error, a bi
Externí odkaz:
http://arxiv.org/abs/2110.02421
Since most industrial control applications use PID controllers, PID tuning and anti-windup measures are significant problems. This paper investigates tuning the feedback gains of a PID controller via back-calculation and automatic differentiation too
Externí odkaz:
http://arxiv.org/abs/2106.10516