Zobrazeno 1 - 10
of 42
pro vyhledávání: '"Liebman, Elad"'
Autor:
Voytan, Dimitri P., Ravula, Sriram, Ardel, Alexandru, Liebman, Elad, Dhara, Arnab, Sen, Mrinal K., Dimakis, Alexandros
Seismic images often contain both coherent and random artifacts which complicate their interpretation. To mitigate these artifacts, we introduce a novel unsupervised deep-learning method based on Deep Image Prior (DIP) which uses convolutional neural
Externí odkaz:
http://arxiv.org/abs/2405.17597
Autor:
Liebman, Elad
Fault detection is a key challenge in the management of complex systems. In the context of SparkCognition's efforts towards predictive maintenance in large scale industrial systems, this problem is often framed in terms of anomaly detection - identif
Externí odkaz:
http://arxiv.org/abs/2405.17488
Current approaches to learning cooperative multi-agent behaviors assume relatively restrictive settings. In standard fully cooperative multi-agent reinforcement learning, the learning algorithm controls $\textit{all}$ agents in the scenario, while in
Externí odkaz:
http://arxiv.org/abs/2404.10740
Autor:
Liebman, Elad, Stone, Peter
Past research has clearly established that music can affect mood and that mood affects emotional and cognitive processing, and thus decision-making. It follows that if a robot interacting with a person needs to predict the person's behavior, knowledg
Externí odkaz:
http://arxiv.org/abs/2308.14269
Current approaches to multi-agent cooperation rely heavily on centralized mechanisms or explicit communication protocols to ensure convergence. This paper studies the problem of distributed multi-agent learning without resorting to centralized compon
Externí odkaz:
http://arxiv.org/abs/2206.00233
Autor:
Liebman, Elad, Stone, Peter
Computers have been used to analyze and create music since they were first introduced in the 1950s and 1960s. Beginning in the late 1990s, the rise of the Internet and large scale platforms for music recommendation and retrieval have made music an in
Externí odkaz:
http://arxiv.org/abs/2006.10553
This paper considers the problem of representative selection: choosing a subset of data points from a dataset that best represents its overall set of elements. This subset needs to inherently reflect the type of information contained in the entire se
Externí odkaz:
http://arxiv.org/abs/1502.07428
In recent years, there has been growing focus on the study of automated recommender systems. Music recommendation systems serve as a prominent domain for such works, both from an academic and a commercial perspective. A fundamental aspect of music pe
Externí odkaz:
http://arxiv.org/abs/1401.1880
Autor:
Liebman, Elad1 eladlieb@gmail.com, Saar-Tsechansky, Maytal2 maytal@mail.utexas.edu, Stone, Peter3 pstone@cs.utexas.edu
Publikováno v:
MIS Quarterly. Sep2019, Vol. 43 Issue 3, p765-A6. 28p. 1 Diagram, 5 Charts, 8 Graphs.
