Zobrazeno 1 - 10
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pro vyhledávání: '"Tenenbaum, Josh"'
Autor:
Cano, Leonardo Hernandez, Pu, Yewen, Hawkins, Robert D., Tenenbaum, Josh, Solar-Lezama, Armando
A typical way in which a machine acquires knowledge from humans is by programming. Compared to learning from demonstrations or experiences, programmatic learning allows the machine to acquire a novel skill as soon as the program is written, and, by b
Externí odkaz:
http://arxiv.org/abs/2310.11614
Autor:
Ajay, Anurag, Han, Seungwook, Du, Yilun, Li, Shuang, Gupta, Abhi, Jaakkola, Tommi, Tenenbaum, Josh, Kaelbling, Leslie, Srivastava, Akash, Agrawal, Pulkit
To make effective decisions in novel environments with long-horizon goals, it is crucial to engage in hierarchical reasoning across spatial and temporal scales. This entails planning abstract subgoal sequences, visually reasoning about the underlying
Externí odkaz:
http://arxiv.org/abs/2309.08587
Autor:
O'Connell, Thomas P., Bonnen, Tyler, Friedman, Yoni, Tewari, Ayush, Tenenbaum, Josh B., Sitzmann, Vincent, Kanwisher, Nancy
Humans effortlessly infer the 3D shape of objects. What computations underlie this ability? Although various computational models have been proposed, none of them capture the human ability to match object shape across viewpoints. Here, we ask whether
Externí odkaz:
http://arxiv.org/abs/2308.11300
A single panel of a comic book can say a lot: it can depict not only where the characters currently are, but also their motions, their motivations, their emotions, and what they might do next. More generally, humans routinely infer complex sequences
Externí odkaz:
http://arxiv.org/abs/2305.17195
Great storytellers know how to take us on a journey. They direct characters to act -- not necessarily in the most rational way -- but rather in a way that leads to interesting situations, and ultimately creates an impactful experience for audience me
Externí odkaz:
http://arxiv.org/abs/2305.16913
Autor:
Jin, Zhijing, Levine, Sydney, Gonzalez, Fernando, Kamal, Ojasv, Sap, Maarten, Sachan, Mrinmaya, Mihalcea, Rada, Tenenbaum, Josh, Schölkopf, Bernhard
AI systems are becoming increasingly intertwined with human life. In order to effectively collaborate with humans and ensure safety, AI systems need to be able to understand, interpret and predict human moral judgments and decisions. Human moral judg
Externí odkaz:
http://arxiv.org/abs/2210.01478
How to build AI that understands human intentions, and uses this knowledge to collaborate with people? We describe a computational framework for evaluating models of goal inference in the domain of 3D motor actions, which receives as input the 3D coo
Externí odkaz:
http://arxiv.org/abs/2112.00903
Map Induction: Compositional spatial submap learning for efficient exploration in novel environments
Humans are expert explorers. Understanding the computational cognitive mechanisms that support this efficiency can advance the study of the human mind and enable more efficient exploration algorithms. We hypothesize that humans explore new environmen
Externí odkaz:
http://arxiv.org/abs/2110.12301
Humans have the ability to rapidly understand rich combinatorial concepts from limited data. Here we investigate this ability in the context of auditory signals, which have been evolved in a cultural transmission experiment to study the emergence of
Externí odkaz:
http://arxiv.org/abs/2104.08274
Program synthesis techniques construct or infer programs from user-provided specifications, such as input-output examples. Yet most specifications, especially those given by end-users, leave the synthesis problem radically ill-posed, because many pro
Externí odkaz:
http://arxiv.org/abs/2007.05060