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pro vyhledávání: '"Fetaya, Ethan"'
One of the challenges in applying reinforcement learning in a complex real-world environment lies in providing the agent with a sufficiently detailed reward function. Any misalignment between the reward and the desired behavior can result in unwanted
Externí odkaz:
http://arxiv.org/abs/2402.11367
Autor:
Shamsian, Aviv, Navon, Aviv, Zhang, David W., Zhang, Yan, Fetaya, Ethan, Chechik, Gal, Maron, Haggai
Learning in deep weight spaces (DWS), where neural networks process the weights of other neural networks, is an emerging research direction, with applications to 2D and 3D neural fields (INRs, NeRFs), as well as making inferences about other types of
Externí odkaz:
http://arxiv.org/abs/2402.04081
As machine learning becomes more prominent there is a growing demand to perform several inference tasks in parallel. Running a dedicated model for each task is computationally expensive and therefore there is a great interest in multi-task learning (
Externí odkaz:
http://arxiv.org/abs/2402.04005
Autor:
Shamsian, Aviv, Zhang, David W., Navon, Aviv, Zhang, Yan, Kofinas, Miltiadis, Achituve, Idan, Valperga, Riccardo, Burghouts, Gertjan J., Gavves, Efstratios, Snoek, Cees G. M., Fetaya, Ethan, Chechik, Gal, Maron, Haggai
Learning in weight spaces, where neural networks process the weights of other deep neural networks, has emerged as a promising research direction with applications in various fields, from analyzing and editing neural fields and implicit neural repres
Externí odkaz:
http://arxiv.org/abs/2311.08851
Permutation symmetries of deep networks make basic operations like model merging and similarity estimation challenging. In many cases, aligning the weights of the networks, i.e., finding optimal permutations between their weights, is necessary. Unfor
Externí odkaz:
http://arxiv.org/abs/2310.13397
The task of open-vocabulary object-centric image retrieval involves the retrieval of images containing a specified object of interest, delineated by an open-set text query. As working on large image datasets becomes standard, solving this task effici
Externí odkaz:
http://arxiv.org/abs/2309.14999
In computer vision and machine learning, a crucial challenge is to lower the computation and memory demands for neural network inference. A commonplace solution to address this challenge is through the use of binarization. By binarizing the network w
Externí odkaz:
http://arxiv.org/abs/2307.01683
The field of generative models has recently witnessed significant progress, with diffusion models showing remarkable performance in image generation. In light of this success, there is a growing interest in exploring the application of diffusion mode
Externí odkaz:
http://arxiv.org/abs/2306.11173
Lip-to-speech involves generating a natural-sounding speech synchronized with a soundless video of a person talking. Despite recent advances, current methods still cannot produce high-quality speech with high levels of intelligibility for challenging
Externí odkaz:
http://arxiv.org/abs/2306.03258
Referring Expressions Generation (REG) aims to produce textual descriptions that unambiguously identifies specific objects within a visual scene. Traditionally, this has been achieved through supervised learning methods, which perform well on specifi
Externí odkaz:
http://arxiv.org/abs/2305.19108