Zobrazeno 1 - 10
of 216
pro vyhledávání: '"Fiete, Ila"'
Diffusion models are capable of generating photo-realistic images that combine elements which likely do not appear together in the training set, demonstrating the ability to compositionally generalize. Nonetheless, the precise mechanism of compositio
Externí odkaz:
http://arxiv.org/abs/2408.13256
Much research in machine learning involves finding appropriate inductive biases (e.g. convolutional neural networks, momentum-based optimizers, transformers) to promote generalization on tasks. However, quantification of the amount of inductive bias
Externí odkaz:
http://arxiv.org/abs/2406.15941
Frontier AI systems are making transformative impacts across society, but such benefits are not without costs: models trained on web-scale datasets containing personal and private data raise profound concerns about data privacy and security. Language
Externí odkaz:
http://arxiv.org/abs/2406.14549
To generate coherent responses, language models infer unobserved meaning from their input text sequence. One potential explanation for this capability arises from theories of delay embeddings in dynamical systems, which prove that unobserved variable
Externí odkaz:
http://arxiv.org/abs/2406.11993
State estimation is crucial for the performance and safety of numerous robotic applications. Among the suite of estimation techniques, particle filters have been identified as a powerful solution due to their non-parametric nature. Yet, in high-dimen
Externí odkaz:
http://arxiv.org/abs/2404.13698
Autor:
Schaeffer, Rylan, Zahedi, Nika, Khona, Mikail, Pai, Dhruv, Truong, Sang, Du, Yilun, Ostrow, Mitchell, Chandra, Sarthak, Carranza, Andres, Fiete, Ila Rani, Gromov, Andrey, Koyejo, Sanmi
Associative memory and probabilistic modeling are two fundamental topics in artificial intelligence. The first studies recurrent neural networks designed to denoise, complete and retrieve data, whereas the second studies learning and sampling from pr
Externí odkaz:
http://arxiv.org/abs/2402.10202
Diffusion models are capable of impressive feats of image generation with uncommon juxtapositions such as astronauts riding horses on the moon with properly placed shadows. These outputs indicate the ability to perform compositional generalization, b
Externí odkaz:
http://arxiv.org/abs/2402.03305
Work on deep learning-based models of grid cells suggests that grid cells generically and robustly arise from optimizing networks to path integrate, i.e., track one's spatial position by integrating self-velocity signals. In previous work, we challen
Externí odkaz:
http://arxiv.org/abs/2312.03954
Autor:
Schaeffer, Rylan, Khona, Mikail, Ma, Tzuhsuan, Eyzaguirre, Cristóbal, Koyejo, Sanmi, Fiete, Ila Rani
To solve the spatial problems of mapping, localization and navigation, the mammalian lineage has developed striking spatial representations. One important spatial representation is the Nobel-prize winning grid cells: neurons that represent self-locat
Externí odkaz:
http://arxiv.org/abs/2311.02316
Deep reinforcement learning methods exhibit impressive performance on a range of tasks but still struggle on hard exploration tasks in large environments with sparse rewards. To address this, intrinsic rewards can be generated using forward model pre
Externí odkaz:
http://arxiv.org/abs/2310.17537