Zobrazeno 1 - 10
of 2 366
pro vyhledávání: '"Ila, P."'
Sequential learning in deep models often suffers from challenges such as catastrophic forgetting and loss of plasticity, largely due to the permutation dependence of gradient-based algorithms, where the order of training data impacts the learning out
Externí odkaz:
http://arxiv.org/abs/2410.22695
Autor:
Hwang, Jaedong, Cheung, Brian, Hong, Zhang-Wei, Boopathy, Akhilan, Agrawal, Pulkit, Fiete, Ila
Highly performant large-scale pre-trained models promise to also provide a valuable foundation for learning specialized tasks, by fine-tuning the model to the desired task. By starting from a good general-purpose model, the goal is to achieve both sp
Externí odkaz:
http://arxiv.org/abs/2410.21582
Autor:
Qian, Daoyuan, Fiete, Ila
Reservoir computing (RC) harnesses the intrinsic dynamics of a chaotic system, called the reservoir, to perform various time-varying functions. An important use-case of RC is the generation of target temporal sequences via a trainable output-to-reser
Externí odkaz:
http://arxiv.org/abs/2410.20393
This paper presents DynORecon, a Dynamic Object Reconstruction system that leverages the information provided by Dynamic SLAM to simultaneously generate a volumetric map of observed moving entities while estimating free space to support navigation. B
Externí odkaz:
http://arxiv.org/abs/2409.19928
Eye-tracking technology has gained significant attention in recent years due to its wide range of applications in human-computer interaction, virtual and augmented reality, and wearable health. Traditional RGB camera-based eye-tracking systems often
Externí odkaz:
http://arxiv.org/abs/2409.18813
Autor:
Boopathy, Akhilan, Fiete, Ila
As neural networks continue to grow in size but datasets might not, it is vital to understand how much performance improvement can be expected: is it more important to scale network size or data volume? Thus, neural network scaling laws, which charac
Externí odkaz:
http://arxiv.org/abs/2409.05782
Modular neural networks outperform nonmodular neural networks on tasks ranging from visual question answering to robotics. These performance improvements are thought to be due to modular networks' superior ability to model the compositional and combi
Externí odkaz:
http://arxiv.org/abs/2409.05780
Publikováno v:
Advances in Neural Information Processing Systems 2024
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 \textit{compositionally generalize}. Nonetheless, the precise mechanism of c
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