Zobrazeno 1 - 10
of 576
pro vyhledávání: '"Kramer, Stefan"'
We propose soft Hoeffding trees (SoHoT) as a new differentiable and transparent model for possibly infinite and changing data streams. Stream mining algorithms such as Hoeffding trees grow based on the incoming data stream, but they currently lack th
Externí odkaz:
http://arxiv.org/abs/2411.04812
Large Language Models (LLMs) frequently lack domain-specific knowledge and even fine-tuned models tend to hallucinate. Hence, more reliable models that can include external knowledge are needed. We present a pipeline, 4StepFocus, and specifically a p
Externí odkaz:
http://arxiv.org/abs/2409.00861
Fair Representation Learning (FRL) is a broad set of techniques, mostly based on neural networks, that seeks to learn new representations of data in which sensitive or undesired information has been removed. Methodologically, FRL was pioneered by Ric
Externí odkaz:
http://arxiv.org/abs/2407.03834
Autor:
Derstroff, Cedric, Brugger, Jannis, Blüml, Jannis, Mezini, Mira, Kramer, Stefan, Kersting, Kristian
Monte-Carlo tree search (MCTS) is an effective anytime algorithm with a vast amount of applications. It strategically allocates computational resources to focus on promising segments of the search tree, making it a very attractive search algorithm in
Externí odkaz:
http://arxiv.org/abs/2402.08511
Publikováno v:
AAAI, vol. 38, no. 10, pp. 11766-11774, Mar. 2024
Peer learning is a novel high-level reinforcement learning framework for agents learning in groups. While standard reinforcement learning trains an individual agent in trial-and-error fashion, all on its own, peer learning addresses a related setting
Externí odkaz:
http://arxiv.org/abs/2312.09950
The paper surveys automated scientific discovery, from equation discovery and symbolic regression to autonomous discovery systems and agents. It discusses the individual approaches from a "big picture" perspective and in context, but also discusses o
Externí odkaz:
http://arxiv.org/abs/2305.02251
Autor:
Pensel, Lukas, Kramer, Stefan
Multi-relational databases are the basis of most consolidated data collections in science and industry today. Most learning and mining algorithms, however, require data to be represented in a propositional form. While there is a variety of specialize
Externí odkaz:
http://arxiv.org/abs/2211.02363
Autor:
Hauptmann, Tony, Kramer, Stefan
Recent years have seen a surge of novel neural network architectures for the integration of multi-omics data for prediction. Most of the architectures include either encoders alone or encoders and decoders, i.e., autoencoders of various sorts, to tra
Externí odkaz:
http://arxiv.org/abs/2208.14822