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of 108
pro vyhledávání: '"Walker, Jacob A."'
Autor:
Kim, Gwanghyun, Martinez, Alonso, Su, Yu-Chuan, Jou, Brendan, Lezama, José, Gupta, Agrim, Yu, Lijun, Jiang, Lu, Jansen, Aren, Walker, Jacob, Somandepalli, Krishna
Training diffusion models for audiovisual sequences allows for a range of generation tasks by learning conditional distributions of various input-output combinations of the two modalities. Nevertheless, this strategy often requires training a separat
Externí odkaz:
http://arxiv.org/abs/2405.13762
Autor:
Yang, Sherry, Walker, Jacob, Parker-Holder, Jack, Du, Yilun, Bruce, Jake, Barreto, Andre, Abbeel, Pieter, Schuurmans, Dale
Both text and video data are abundant on the internet and support large-scale self-supervised learning through next token or frame prediction. However, they have not been equally leveraged: language models have had significant real-world impact, wher
Externí odkaz:
http://arxiv.org/abs/2402.17139
Autor:
Walker, Jacob, Vértes, Eszter, Li, Yazhe, Dulac-Arnold, Gabriel, Anand, Ankesh, Weber, Théophane, Hamrick, Jessica B.
State of the art reinforcement learning has enabled training agents on tasks of ever increasing complexity. However, the current paradigm tends to favor training agents from scratch on every new task or on collections of tasks with a view towards gen
Externí odkaz:
http://arxiv.org/abs/2302.04009
Autor:
Bošnjak, Matko, Richemond, Pierre H., Tomasev, Nenad, Strub, Florian, Walker, Jacob C., Hill, Felix, Buesing, Lars Holger, Pascanu, Razvan, Blundell, Charles, Mitrovic, Jovana
Learning from large amounts of unsupervised data and a small amount of supervision is an important open problem in computer vision. We propose a new semi-supervised learning method, Semantic Positives via Pseudo-Labels (SemPPL), that combines labelle
Externí odkaz:
http://arxiv.org/abs/2301.05158
Autor:
Nash, Charlie, Carreira, João, Walker, Jacob, Barr, Iain, Jaegle, Andrew, Malinowski, Mateusz, Battaglia, Peter
We present a general-purpose framework for image modelling and vision tasks based on probabilistic frame prediction. Our approach unifies a broad range of tasks, from image segmentation, to novel view synthesis and video interpolation. We pair this f
Externí odkaz:
http://arxiv.org/abs/2203.09494
Autor:
Anand, Ankesh, Walker, Jacob, Li, Yazhe, Vértes, Eszter, Schrittwieser, Julian, Ozair, Sherjil, Weber, Théophane, Hamrick, Jessica B.
One of the key promises of model-based reinforcement learning is the ability to generalize using an internal model of the world to make predictions in novel environments and tasks. However, the generalization ability of model-based agents is not well
Externí odkaz:
http://arxiv.org/abs/2111.01587
Autor:
Banino, Andrea, Badia, Adrià Puidomenech, Walker, Jacob, Scholtes, Tim, Mitrovic, Jovana, Blundell, Charles
Many reinforcement learning (RL) agents require a large amount of experience to solve tasks. We propose Contrastive BERT for RL (CoBERL), an agent that combines a new contrastive loss and a hybrid LSTM-transformer architecture to tackle the challenge
Externí odkaz:
http://arxiv.org/abs/2107.05431
In recent years, the task of video prediction-forecasting future video given past video frames-has attracted attention in the research community. In this paper we propose a novel approach to this problem with Vector Quantized Variational AutoEncoders
Externí odkaz:
http://arxiv.org/abs/2103.01950
Self-supervised learning has emerged as a strategy to reduce the reliance on costly supervised signal by pretraining representations only using unlabeled data. These methods combine heuristic proxy classification tasks with data augmentations and hav
Externí odkaz:
http://arxiv.org/abs/2010.07922
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